The Role of Phenotypic Plasticity in Adaptation to Treatment and Prospective Plasticity Drivers in Hepatoblastoma

The Role of Phenotypic Plasticity in Adaptation to Treatment and Prospective Plasticity Drivers in Hepatoblastoma | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results The Role of Phenotypic Plasticity in Adaptation to Treatment and Prospective Plasticity Drivers in Hepatoblastoma View ORCID Profile Emilia Chen , Christie English , Alejandro Allo Anido , Siân Hamer , Stefano Cairo , View ORCID Profile Alejandra Bruna doi: https://doi.org/10.1101/2025.04.16.648783 Emilia Chen 1 Centre for Evolution and Cancer, The Institute of Cancer Research , London, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Emilia Chen Christie English 1 Centre for Evolution and Cancer, The Institute of Cancer Research , London, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Alejandro Allo Anido 1 Centre for Evolution and Cancer, The Institute of Cancer Research , London, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Siân Hamer 1 Centre for Evolution and Cancer, The Institute of Cancer Research , London, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Stefano Cairo 2 Fondazione Istituto di Ricerca Pediatrica Città della Speranza (IRP) , Padova, Italy 3 Champions Oncology, Inc. , Hackensack, New Jersey, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Alejandra Bruna 1 Centre for Evolution and Cancer, The Institute of Cancer Research , London, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Alejandra Bruna For correspondence: alejandra.bruna{at}icr.ac.uk Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Hepatoblastoma (HB) is a paediatric liver cancer, associated with one of the lowest mutational burdens compared to other cancers. Despite this, HBs exhibit diverse phenotypes. It has been proposed that hijacking of early developmental plasticity is especially relevant to chemotherapy response in childhood cancers, however the underlying mechanisms of plasticity in HB remain poorly understood. Plasticity in HB requires investigation after treatment at a more granular temporal scale to further elucidate its role in treatment adaptation. In this work, we aim to study the role of plasticity in treatment adaptation and its underlying mechanisms by integrating heritable and expressed static DNA barcode technology with single-cell sequencing. The synergy between these approaches allows for simultaneous lineage tracing of single clones and phenotyping of single cells. We identify the phenotypic states that exist in HB preclinical models and explore the phenotypic transitions that occur following treatment, which could be important for cells to survive, persist and eventually recover. Expression and accessibility landscapes reveal prospective targets that could be markers or drivers of the plasticity which enables treatment adaptation in HB. By homing in on the clonal, transcriptomic and epigenetic dynamics post treatment, these results examine in greater detail the role of plasticity in treatment adaptation in HB. Introduction Hepatoblastoma (HB) is the most common liver cancer in young children, typically developing within the first five years of life on nonfibrotic liver (Darbari 2003). Although rare, occurring in approximately one per million children per year, in recent years the incidence rate has been increasing while treatment options for chemo-resistant tumours remain limited ( Feng et al. 2019 ; Hubbard et al. 2019 ; Wang et al. 2025 ). Paediatric cancers have a lower mutational burden than adult cancers, with HB having one of the lowest frequencies of somatic coding mutations among paediatric cancer types (Gröbner et al. 2018). The vast majority of HBs carry mutations that affect the Wnt pathway, with 80% of HBs carrying mutations in CTNNB1 that activate β -catenin ( Koch et al. 1999 ). However, aside from β -catenin activating alterations, there are few other driver mutations ( Hirsch et al. 2021 ; Nagae et al. 2021 ). Despite a lack of genetic driver mutations, HB tumours are phenotypically heterogeneous. Based on expression data, HBs have been previously split into two subtypes: C1 and C2 ( Cairo et al. 2008 ). Although both have a similar CTNNB1 mutation profile, their gene expression landscapes are distinct; the C1 subtype corresponds to a well-differentiated foetal histotype and a better prognosis, while C2 subtypes typically exhibit predominantly immature histotypes and a worse prognosis. Further stratification of these transcriptomic subtypes, as well as incorporation of epigenetic profiling, has since enhanced this classification ( Carrillo-Reixach et al. 2020 ; Hirsch et al. 2021 ; Hooks et al. 2018 ; Sekiguchi et al. 2020; Song et al. 2022 ; Sumazin et al. 2022 ). The heterogeneity in this developmental tumour, driven by few mutations, makes HB an interesting candidate to study the role of plasticity in facilitating adaptation to chemotherapy. Plasticity is the production of different phenotypes by the same genotype, under different environmental conditions ( Schlichting and Pigliucci 1998 ; West-Eberhard 2003 ). In HB, it has been shown that stem-cell like cells, induced during chemotherapy, have the ability to switch between subtypes and could promote repopulation after treatment ( Lee-Theilen et al. 2022 ). Furthermore, recent single-cell sequencing studies in HB have supported switching between phenotypes and provided evidence that plasticity could drive re-differentiation from progenitor to more hepatocytic states after treatment ( Huang et al. 2022 ; Roehrig et al. 2024; Song et al. 2022 ). However, attention on the post-treatment period, when cells begin to recover, at a more granular temporal resolution is required to further understand the role of plasticity in treatment adaptation and elucidate molecular mechanisms that could drive adaptive phenotypic switches in HB. In this work, we combine single-cell RNA sequencing (scRNA-seq) and single-nuclei multiome sequencing (snRNA-seq and snATAC-seq) with expressible DNA static barcodes in HB preclinical models. Integrating these technologies, allows us to track phenotypic changes over the course of cellular recovery from a chemotherapeutic stress. Through this, we add to the characterisation of plasticity in HB and its wider role in cancer evolution. We also explore prospective markers and drivers of plasticity that could play a role in treatment adaptation in HB. Results Single-cell analysis reveals HB cells utilise progenitor-like phenotypic states to survive and recover from treatment To roadmap plasticity following chemotherapy, we used scRNA-seq to characterise the phenotypic changes that occur in HB cell lines after treatment with cisplatin. We barcoded HB cell lines (HuH6 and HepG2) at a multiplicity of infection (MOI), such that one cell would have one unique bar- code. Each barcoded cell line was expanded to produce the population (POT) from which we seeded each experiment. Cells were treated with cisplatin for 48 hours, initiating surviving cells to enter a drug-tolerant persister (DTP) state (persistence phase). DTP cells were kept in culture, allowing transcriptomic changes to take place (awakening phase), which facilitated a return to cultures that were morphologically the same as pre-treatment cells and doubling at their baseline rates (recovery phase; Fig. 1a ). We took sequential samples during the course of the experiment for sequencing. Download figure Open in new tab Figure 1: (a) Schematic of the single-cell experiment design. 1 × 10 6 HuH6 cells were barcoded at a multiplicity of infection (MOI) of 0.1, then expanded × 233, and seeded into 15 (five triplicates) T175 flasks. Flasks were treated with cisplatin or NaCl (solvent control) and left to recover until cultures began doubling at their baseline rates. (b) scRNA-seq UMAP of all 10,791 HuH6 cells that passed quality control (QC), coloured by annotated Partitioning Around Medoids (PAM) cluster. Differentiation schematic shows relation to states of normal hepatocyte differentiation. (c) scRNA-seq UMAP of all 10,791 HuH6 cells that passed QC, coloured by experimental timepoint. (d) scRNA-seq UMAP of all 10,791 HuH6 cells that passed QC, coloured by C2 signature expression. (e) Violin plot showing the C2 signature expression at each experimental timepoint. (f) Bar chart of PAM cluster proportions at each experimental timepoint. Colour indicates PAM cluster annotation. The scRNA-seq data revealed clusters that were associated with the stages of normal liver de- velopment and distinct to the different phases of persistence, awakening and recovery. In the HuH6 data, Partitioning Around Medoids (PAM) clustering revealed six clusters, which we annotated using canonical marker genes of liver development (Lemaigre 2009; Si-Tayeb, Lemaigre, and Duncan 2010 ; Zong and Stanger 2012 ; Fig. 1b ; Supplementary Fig. 1a-c). We observed that more differentiated Hepatocytic (1-3) clusters were mainly composed of cells from the untreated and recovered (Recovery t 3 ) timepoints and showed an enrichment of the C2 signature. On the other hand, Early and Late Progenitor, and Stem (cell)-like clusters were mostly made up of cells sampled during the persistence and awakening phases (Cisplatin, Recovery t 1 , Recovery t 2 ) and expressed relatively lower levels of the C2 signature genes ( Fig. 1c-e ). This suggests that the more differentiated clusters we observed in the untreated population and after recovery are more proliferative and show similarities to aggressive C2-like phenotypes. However, it is the less differentiated clusters that survived and persisted following treatment, despite being less proliferative and less enriched for the C2 signature. We saw temporal changes in the cluster composition of the population over the course of the persistence, awakening and recovery phases ( Fig. 1f ). Before treatment, more differentiated clusters dominated, but there is heterogeneity in the population, with less differentiated groups of cells also being present. Following cisplatin treatment, there is an enrichment in the population for cells that belong to less differentiated cluster types; initially an enrichment for the Late Progenitor cluster during the persistence phase, then for the Early Progenitor and Stem-like clusters during the awakening and recovery phases. Later, this small population of cells, expressing hepatoblast and hepatic stem cell markers, exhibited the ability to recapitulate the phenotypic diversity observed prior to treatment. Plasticity enables a small number of clones to recapitulate transcriptomic hetero- geneity after a treatment-induced bottleneck We assessed if the cells that survived and persisted after treatment phenotypically transitioned to more differentiated-like states to regenerate the phenotypic heterogeneity of the initial population. In order to achieve this, we explored the DNA barcode data sequenced from the same persistence- awakening-recovery experiment. One barcode (clone) is representative of the daughter cells from one parental cell, i.e. the cell which was initially barcoded. We were able to track the same clones across replicates and conditions as the initial barcoded population was expanded to produce a population with multiple daughter cells from the same clone (POT), from which the experiment was seeded from to achieve representation across flasks ( Acar et al. 2020 ). Using the DNA barcode data, we explored the clonal landscape during the persistence, awakening and recovery phases to assess the importance of plasticity relative to clonal inheritance. We observed that a small number of clones were responsible for the reemergence of Hepatocytic clusters during the recovery phase of the culture ( Fig. 2a ). This is further illustrated in the beta diversity of the recovered (Recovery t 3 ) replicates, which was strikingly different to earlier timepoints ( Fig. 2b ). However, we have already seen that transcriptomic cluster diversity is restored. Thus, this suggests that single clones underwent phenotypic transitions to recapitulate the transcriptomic heterogeneity of the population before treatment-induced selection for Progenitor-like phenotypes. Furthermore, we saw the clones that were enriched in the recovered (Recovery t 3 ) population were different between replicates, indicating that the recovery of clones is stochastic. This implies clonal drift, opposed to selection, dominated during recovery, and gave rise to a less heterogenous clonal population. However, phenotypic plasticity allows for single clones to occupy a diverse set of transcriptomic states, enabling maintenance of phenotypic heterogeneity in the recovered population. Download figure Open in new tab Figure 2: (a) Percentage abundance of a sample of unique DNA barcodes (1943/102171), which are represented in the top 5% by abundance in the population of at least one replicate, tracked over persistence, awakening and recovery phases. (b) Principal Coordinate Analysis (PCoA) plot of the beta diversity, based on the Bray-Curtis dissimilarity, of the DNA barcode populations between replicates. (c) Compound bubble-pie chart of 26 expressed DNA barcodes (rows) at each experimental timepoint (columns). The size of the bubble-pie represents the numbers of cells with a given barcode at a given experimental timepoint. The relative sector size for each bubble-pie represents the proportion of cells belonging to an annotated PAM cluster (colour), for a given barcode at a given experimental timepoint. We utilised the expression of the DNA barcodes to further investigate if plasticity within the same clonal population drove the reemergence of a diverse transcriptomic population during recovery. We integrated the clonal information from the expressed barcodes with phenotypic identity, derived from the scRNA-seq data. We saw that the phenotypic dynamics of most clones mirrors that of the overall population; initially there is a heterogeneous population dominated by Hepatocytic clusters, following treatment there is an enrichment for Progenitors, and finally clones that recover begin recapitulating the heterogeneity of the population prior to treatment ( Fig. 2c ). Some clones show complete swings of the population between phenotypes. For example, the population of clone 2 during the persistence phase (Cisplatin) is only composed of Late Progenitor cells, then during awakening (Recovery t 1 , Recovery t 2 ) is dominated by Early Progenitor and Stem-like phenotypes, before finally in the recovered (Recovery t 3 ) population the reemergence of more Hepatocytic phenotypes is observed. This further validates that plasticity, opposed to clonal identity, drives recovery from treatment. Distinct groups of genes exhibit expression patterns that correlate with recovery from cisplatin and are prospective plasticity markers in HB Using pseudotime inference methods, we can establish genes whose patterns of expression correlate with the phenotypic plasticity we observe during progression through the persistence, awakening and recovery phases – ‘recovery plasticity’. Using the R package Slingshot, we can assign a pseudotime ordering for cells that represents the transitions we expect during persistence, awakening and recovery ( Street et al., 2018 ). We rooted pseudotime to the Late Progenitor cluster, the enriched phenotype dur- ing persistence ( Fig. 3a ). The three resulting Slingshot lineages were representative of the transitions we identified experimentally (recovery lineages 1-3), i.e. follow transitions from the Late Progenitor cluster to a Hepatocytic cluster. For each recovery lineage, we found genes that exhibited expression patterns correlating with pseudotime progression, therefore associated with recovery plasticity (Sup- plementary Fig. 3a ). Across all three lineages, the expression of similar genes was observed to correlate with recovery plasticity ( Fig. 3b ). Genes that showed a gradual increase in expression were mostly related to liver metabolic processes (e.g. SCD , AFP , APOA1 ) and proliferation (e.g. TOP2A , NCL , HMGB2 ), while genes that exhibited a gradual decrease were enriched for epithelial-to-mesenchymal (EMT) transition, (e.g. CD44 , SERPINE1 , SAT1 ), as well as cisplatin-related responses, such as DNA repair and apoptosis (e.g. MDM2 , ATF3 , SFN ). Download figure Open in new tab Figure 3: (a) scRNA-seq UMAP of all 10,791 HuH6 cells that passed QC, coloured by computed shared pseudotime value. Black lines represent recovery lineages 1-3. (b) GSEA plots for MSigDB Hallmark terms that are associated with the genes whose expression, either increasing (up) or de- creasing (down), correlated with pseudotime progression (pseudotime-derived genes). (c) scRNA-seq UMAP of all 43,017 HepG2 cells that passed QC, coloured by annotated PAM cluster. (d) Jaccard similarity between the pseudotime-derived genes identified in the HuH6 and HepG2 scRNA-seq data. (e) Expression of the Plasticity Up and Down markers in scRNA-seq data from six patient samples (Roehrig et al. 2024). Heatmap showing expression of plasticity markers in pseudobulked tumour and non-tumour annotated cells. (f) Expression of plasticity markers in cells, pseudobulked by cell states: Hepatocytic (H), Hepatocytic+Liver Progenitor (H+LP), Liver Progenitor (LP), and Mesenchymal (M). Repeating the same analysis in a different cell line (HepG2) revealed similar groups of genes are associated with a transition from less to more differentiated-like clusters. We repeated a similar experimental setup on the HepG2 cell line, with one fewer recovery sample. We observed similar clusters and dynamics during early recovery in the scRNA-seq data ( Fig. 3c ; Supplementary Fig. 3b-c). When repeating the pseudotime analysis on the HepG2 data, we also find similar gene sets enriched; for genes increasing in expression, there is an enrichment for proliferation and MYC-related gene sets, while for genes decreasing, an enrichment for DNA repair, apoptosis and TGF- β signalling was observed ( Fig. 3d ; Supplementary Fig. 3d). To further validate the clinical relevance of pseudotime-derived genes, we looked at their expression in a published scRNA-seq dataset of six HB patient samples (Roehrig et al. 2024). We first found the genes common to all three recovery lineages in the HuH6 data that positively correlated with plasticity during recovery, i.e. increased in expression as pseudotime progressed (Plasticity Up), and applied the same criteria for the genes that decreased in expression (Plasticity Down). In the patient data, Plasticity Up markers were more expressed in the tumour samples, while Plasticity Down genes showed relatively less expression in tumour samples and were even expressed in non-tumour liver ( Fig. 3e ). Across the four different states outlined in the patient data, Mesenchymal (M), Liver Progenitor (LP), Hepatocytic+Liver Progenitor (H+LP) and Hepatocytic (H), we see different patterns of expression for Plasticity Up genes compared to Plasticity Down genes ( Fig. 3f ). For Plasticity Up markers, they appeared more highly expressed in cells from LP samples, which is the state closest to the Hepatocytic clusters in our cell line data (Supplementary Fig. 3e). On the other hand, Plasticity Down markers were more expressed in cells of the M state and less expressed in the H and LP states. Given the derivation of the Plasticity Up and Down markers from cisplatin recovery in cell lines, the fact they illustrate the plasticity between the M and LP poles in patients may implicate M-LP plasticity as a post-treatment response in patients. Expression and accessibility patterns of candidate plasticity drivers and their as- sociation with recurrent HB In order to explore candidate drivers of plasticity, we found the transcription factors that target the pseudotime-derived markers of plasticity during recovery. We carried out transcription factor motif enrichment analysis on the plasticity markers that increased in expression as pseudotime increased. Across all three recovery lineages, a group of 24 enriched transcription factor motifs were shared between the marker lists ( Fig. 4a ). Furthermore, we see these transcription factor motifs enriched in the HepG2 pseudotime data, also in the genes predicted to increase during recovery ( Fig. 4b ). Download figure Open in new tab Figure 4: (a) Venn diagram of enriched transcription factor motifs shared between genes that increase in expression as pseudotime increases for each of the recovery lineages (1-3). (b) Venn diagram of enriched transcription factor motifs shared between genes that increase in expression as pseudotime increases, computed for lineages in both the HuH6 and HepG2 scRNA-seq data. (c) Expression of the 24 shared transcription factors at each experimental timepoint in the HuH6 scRNA-seq data. (d) Expression of Plasticity Up markers at each experimental timepoint in the HuH6 scRNA-seq data. (e) Averaged frequency of sequenced DNA fragments for more accessible peaks in recovered compared to untreated cells. BIRC5 , CKS2 , ERBB2 , FOXA1 and KPNA2 are Plasticity Up markers and targets of the candidate plasticity drivers E2F4, FOS, and YBX1. (f) BIRC5 , CKS2 , ERBB2 , FOXA1 and KPNA2 expression in RNA sequencing data of 66 samples from 32 patients, annotated by sample type (HB, HB recurrence, non-tumour; Carrillo-Reixach et al. 2020 ). To explore these potential plasticity drivers further, we explored the expression of the transcription factors themselves, as well as the expression and accessibility of their targets. We first compared the expression of the 24 transcription factors and the Plasticity Up genes in the scRNA-seq data ( Fig. 4c-d ). We would expect the expression of drivers of recovery plasticity to peak in the awakening or recovery phases, while the markers of recovery plasticity (Plasticity Up genes) would be comparably expressed in the untreated and recovered populations. We observed that the expression of Plasticity Up genes was higher in the untreated and recovered (Recovery t 3 ) populations compared to persistence and awakening (Cisplatin, Recovery t 1 , Recovery t 2 ) timepoints, suggesting their expression signifies the cells returning to their original states. Out of our initial 24 transcription factors, we found seven transcription factors ( CEBPZ , DRAP1 , E2F4 , E2F6 , FOS , IRF3 , YBX1 ) that peak in expression during late awakening (Recovery t 2 ) and/or in the recovered (Recovery t 3 ) population, indicating these could be drivers that facilitate recovery plasticity, by increasing the expression of Plasticity Up genes. For these prospective transcription factor plasticity drivers, we further investigated the accessi- bility of their targets in single-nuclei multiome data of HuH6 cells at the untreated and recovered (Recovery t 3 ) timepoints. We analysed the differential accessibility between recovered and untreated samples for Plasticity Up markers known to be targets of the seven transcription factors (CEBPZ, DRAP1, E2F4, E2F6, FOS, IRF3, YBX1) we had identified in the scRNA-seq data. We see that the Plasticity Up targets of E2F4, E2F6, FOS, IRF3, and YBX1 were more accessible in the recovered compared to the untreated samples. This supports that the activity of these five transcription factors is associated with recovery, rather than being a symptom of the transcriptomic similarity between the untreated and recovered populations. For the list of differentially accessible Plasticity Up targets, we plotted the aggregated signal of sequenced DNA fragments for cells grouped by experimental time- point ( Fig. 4e ). We observe subtle but noticeable differences in chromatin accessibility at five targets ( BIRC5 , CKS2 , ERBB2 , FOXA1 , KPNA2 ), which are regulated by the plasticity drivers E2F4, FOS, and YBX1. While the transcriptome returns to its pre-treatment state, these five targets exhibit a prolonged epigenetic signal following cisplatin treatment. Therefore, BIRC5 , CKS2 , ERBB2 , FOXA1 and KPNA2 could be involved in eventual epigenetic reprogramming of cells to adapt to treatment – epigenetic memory – thus are candidate ‘memory genes’. Finally, we investigated the association between the expression of the prospective memory genes with survival data in HB patients. In published RNA sequencing data of 66 samples from 32 patients with HB, we looked at the expression of BIRC5 , CKS2 , ERBB2 , FOXA1 , KPNA2 in the samples annotated by sample type (HB, HB recurrence, non-tumour; Carrillo-Reixach et al. 2020 ). We observe that the expression of all five genes is increased in the HB samples from recurrences ( Fig. 4f ). This association between the expression of the candidate memory genes and recurrent disease could be due to adaptation to treatment through epigenetic reprogramming. Discussion Heterogeneity in HB has begun to be resolved at the single-cell level ( Huang et al. 2022 ; Roehrig et al. 2024; Song et al. 2022 ). Here, we add to this characterisation by analysing the single-cell transcriptome pre- and post-treatment, encapsulating the phenotypic changes that take place as cells survive, persist through, and recover from cisplatin. We identified phenotypes, resembling hepatoblasts and hepatic stem cells, that become enriched on treatment and during recovery. From these less differentiated-like cells, we see that the diversity of the population pre-treatment can reemerge. We used DNA barcodes to track clonal lineages in our experimental setup of persistence, awakening and recovery, and saw that plasticity, opposed to clonal identity, was more dominant in facilitating recovery from treatment. We also identified markers of plasticity during recovery and explored the transcription factor drivers of these genes in our single-nuclei multiome data, which represent prospective targets for future investigation. Single-cell omics has facilitated a growing recognition of phenotypic plasticity in cancer evolution and its impact on therapy response ( Da Silva-Diz et al. 2018 ; Qin et al. 2020 ). In paediatric can- cers, phenotypic plasticity is of particular interest due to the developmental origin of tumours and the intrinsic plasticity of their cells of origin. We find that first progenitor-like cells persist after a treatment-induced bottleneck, then transitions from less to more differentiated-like phenotypes oc- cur and appear to be important for repopulation. The stem-like qualities of the cells that survive treatment may be co-opted to provide a reservoir of accessible phenotypes to transition to, enabling recovery from treatment. However, it is worth noting that we see a spectrum of phenotypes exists prior to treatment, suggesting that treatment is not itself inducing phenotypic changes, rather acting on phenotype differences that arise independently – ‘phenotypic noise’ ( Whiting et al. 2024 ). Using our scRNA-seq and single-nuclei multiome data, we explored the probable markers and drivers of phenotypic transitions associated with recovery from treatment. Together, our transcriptome and epigenome data highlighted the transcription factors E2F4, FOS, and YBX1 as prospective drivers of the transitions from less to more differentiated-like phenotypes, which are central to phenotypic plasticity during recovery in our HB preclinical models. From our plasticity markers, we then identified BIRC5 , CKS2 , ERBB2 , FOXA1 , KPNA2 as also targets of these drivers. These targets have well known roles in cell cycle regulation, apoptosis and chemoresistance, thus have been identified as targets and/or biomarkers across different cancer types ( Bernardo and Keri 2012 ; Christiansen and Dyrskjøt 2013 ; F. Li, Aljahdali, and Ling 2019 ; Robichaux et al. 2019 ; Borgo et al. 2021 ). Furthermore, these genes were differentially accessible between the recovered versus untreated populations, alluding to there being changes in chromatin accessibility that could be further fine-tuned, conceivably after repeated exposure to stimuli, to result in mitotically heritable changes in responsiveness to cisplatin, a type of epigenetic memory ( Shen et al. 2012 ; D’Urso and Brickner 2014 ). In accordance with this, we observed an association between the expression of these candidate memory genes and recurrent disease in patient data ( Carrillo-Reixach et al. 2020 ). Implementing our integrated experimental approaches of post-treatment sampling, barcoding and single-cell sequencing in more preclinical models will be useful to further our findings on the role plasticity has in enabling recovery from treatment, as well as provide further evidence to support the function of prospective drivers of plasticity during recovery and their targets. Further optimisation of barcoding strategies would also ensure sampling at each timepoint can capture more of the population, i.e. using a smaller pool of unique barcodes and correspondingly, a smaller starting population of cells to compensate for the limited number of cells that can enter the scRNA-seq protocol. In conclusion, we have added granularity to the characterisation of plasticity in HB, crucially post-treatment, by investigating the clonal, transcriptomic and epigenetic changes underlying the phenotypic transitions that take place during recovery from treatment. Methods and Materials Key Resources Table View this table: View inline View popup Method Details Cell culture HuH6 and HepG2 cells were cultured in DMEM, supplemented with 10% FBS and 1% penicillin- streptomycin. All cells were incubated at 37 ◦ C and 5% CO 2 . Lentiviral DNA barcoding Transduction of founder HuH6 and HepG2 cells was carried out as outlined in the CloneTracker XP™ lentiviral barcode libraries user manual. Optimisation to achieve a MOI of 0.1 was carried out. Transduced cells underwent fluorescence-activated cell sorting (FACS) to isolate RFP-positive, barcoded cells. An initial population of 1.26 × 10 5 barcoded HuH6 cells was expanded ( × 233) to form the POT, which was sampled and seeded for the experiment. Similarly, 1.24 × 10 5 barcoded HepG2 cells were expanded ( × 102) to form the POT, which was sampled and seeded for the experiment. DNA barcode analysis The sequencing results were returned as paired FASTQ files, which were then mapped to a custom index reference, generated from all possible sequences of Cellecta barcodes, using BWA. The BAM files were then generated using SamTools. Gene counts were quantified using the featureCounts function from the Rsubread package. Single-cell RNA sequencing (scRNA-seq) Barcoded HuH6 cells were seeded into 15 T175 flasks: three were treated with NaCl, the remaining 12 were treated with cisplatin (20 µ M). After 48 hours, three flasks were sampled, and the remaining cultures were washed three times (1 × PBS, 2 × DMEM) to remove the drug. Recovery samples were taken in triplicate at 7-, 24- and 91-days post treatment. Samples were cryopreserved for 10x Genomics scRNA-seq or frozen down as cell pellets for DNA barcode sequencing. Barcoded HepG2 cells were seeded into 12 T75 flasks: three were treated with NaCl, the remaining nine were treated with cisplatin (8 µ M). After 48 hours, three flasks were sampled, and the remaining cultures were washed three times (1 × PBS, 2 × DMEM) to remove the drug. Recovery samples were taken in triplicate at 10- and 35-days post treatment. Samples were cryopreserved for 10x Genomics scRNA-seq or frozen down as cell pellets for DNA barcode sequencing. scRNA-seq alignment 10x Genomics Cell Ranger was used to process raw sequencing FASTQ files. EmptyDrops from the DropletUtils package was used to remove empty droplets with a false discovery rate of 0.1%. scRNA-seq data analysis Cell-level filtering was carried out with the following thresholds: mitochondrial gene content 5%, and UMI counts > 1000. Filtering at the gene-level was implemented to remove genes without a HGNC ID, as well as those with fewer than five reads. The NormalizeData function from the Seurat package was used to normalise the data. Seurat’s FindVariableFeatures identified highly variable genes (HVGs), which were filtered to remove mitochondrial, long non-coding and ribosomal genes. The data was then scaled using ScaleData from Seurat. A principal component analysis (PCA) was performed on the normalised and scaled data. The batch correction algorithm Harmony was applied, and the output used for Uniform Manifold Approximation and Projection (UMAP)-based dimensional reduction. The data was clustered using the FindClusters function from the Seurat package. Resolutions from 0.2 to 0.8 (in increments of 0.2) were tested for the whole dataset and the data split by experimental timepoint (Condition). FindAllMarkers was used to obtain the cluster markers and pairwise Jaccard similarity was carried out to compare all the clusters in the whole and split datasets. The silhouette function from the cluster package was used to compute silhouette information for k = 2 to k = 15. This was used to inform the value of k to assign when running the function pam from the cluster package on the entire dataset. Markers for PAM clusters were found using Seurat’s FindAllMarkers. Clusters were annotated by their expression of markers of normal liver differentiation (Lemaigre 2009; Si-Tayeb, Lemaigre, and Duncan 2010 ; Zong and Stanger 2012 ). Pseudotime analysis was carried out using the R package Slingshot on the PCA results. The root was set to the Late Progenitor cluster, based on preliminary understanding of recovery from cisplatin in the cell populations. Gene dynamics analysis by fitting a linear spline model to each Slingshot lineage was carried out using TSCAN. Gene set enrichment analysis (GSEA) for Gene Ontology Biological Processes and MSigDB C2 and Hallmark gene sets was performed. Enrichment analysis of transcription factor binding motifs over-represented in gene lists derived from the pseudotime analysis. Gene-motif rankings databases were downloaded from the cisTarget resources website ( https://resources.aertslab.org/cistarget/ ). HGNC motif annotations were loaded and the cisTarget function from the RcisTarget package was used for motif enrichment analysis using the 500bp upstream TSS and TSS +/- 10kbp databases for comparison. Single-nuclei multiome sequencing (snRNA-seq and snATAC-seq) We performed single-nuclei multiome sequencing (snRNA-seq and snATAC-seq) on two untreated and two recovered (Recovery t 3 ) HuH6 replicates. Viable samples were prepared for sequencing following the 10x Genomics Multiome (snRNA-seq and snATAC-seq) protocol. Single-nuclei multiome sequencing alignment We used Cell Ranger ARC to align snRNA-seq and snATAC-seq reads to the human genome (GrCh38/hg38) and generate the matrices of UMI counts per gene and ATAC-seq fragments. snRNA-seq data analysis Cell-level filtering was carried out with the following thresholds: mitochondrial gene content 800. Filtering at the gene-level was implemented to remove genes without a HGNC ID, as well as those with fewer than five reads. The NormalizeData function from the Seurat package was used to normalise the data. Seurat’s FindVariableFeatures identified HVGs, which were filtered to remove mitochondrial, long non- coding and ribosomal genes. The data was then scaled using ScaleData from Seurat. A PCA was performed on the normalised and scaled data. The batch correction algorithm Harmony was applied, and the output used for UMAP-based dimensional reduction. The data was clustered using the FindClusters function from the Seurat package. Resolutions from 0.2 to 0.8 (in increments of 0.2) were tested for the whole dataset and the data split by experimental timepoint (Condition). FindAllMarkers was used to obtain the cluster markers and pairwise Jaccard similarity was carried out to compare all the clusters in the whole and split datasets with the snRNA- seq annotated PAM clusters. The silhouette function from the cluster package was used to compute silhouette information for k = 2 to k = 15. This was used to inform the value of k to assign when running the function pam from the cluster package on the entire dataset. Markers for PAM clusters were found using Seurat’s FindAllMarkers . Clusters were annotated by their expression of markers of normal liver differentiation, as well as their similarity to previously defined scRNA-seq clusters (Lemaigre 2009; Si-Tayeb, Lemaigre, and Duncan 2010 ; Zong and Stanger 2012 ). snATAC-seq data analysis Quality control (QC) filtering was applied ad hoc for each sample on the following metrics: fragment counts, UMI counts, nucleosome signal, and transcription start site (TSS) enrichment. Peak calling was carried out using the function CallPeaks (which relies on MACS peak calling) from the Signac package on each sample separately. Blacklisted peaks and peaks on sex chromosomes were removed. The peak sets were then combined with the reduce function from GenomicRanges, which merges intersecting peaks. The data was merged into a single Signac object with the combined peak set. The resulting peak-cell count matrix was normalised by the Signac term frequency-inverse document frequency (TF-IDF) normalisation procedure ( Cusanovich et al. 2015 ). FindTopFeatures was applied to compute the feature metadata and RunSVD employed for singular value decomposition to return a reduced dimension representation of the object. UMAP dimension reduction for visualisation was applied. Seurat functions FindNeighbours and FindClusters were used to identify clusters between resolutions from 0.2 to 0.8 (in increments of 0.2). Differential accessibility analysis was carried out between recovered and untreated conditions on target genes of the transcription factors of interest, identified in the analysis of the scRNA-seq data. The Signac function CoveragePlot was used to visualise the differentially accessible peaks by computing the averaged frequency of sequenced DNA fragments for cells grouped by experimental timepoint (Condition) within the genomic region of each target gene. Cells in the Signac object were then filtered for cells which also had snRNA-seq information, i.e. were also in the QC filtered Seurat object with annotated cluster metadata. UMAP dimension reduction was rerun on the filtered object, with both the annotated RNA and ATAC information. References ↵ Acar , Ahmet et al. (Apr. 21 , 2020 ). “Exploiting evolutionary steering to induce collateral drug sensitiv- ity in cancer” . In: Nature Communications 11.1, p. 1923 . issn: 2041-1723 . doi: 10.1038/s41467-020-15596-z . OpenUrl CrossRef ↵ Bernardo , Gina M. and Ruth A. Keri (Apr. 1 , 2012 ). “FOXA1: a transcription factor with parallel functions in development and cancer” . In: Bioscience Reports 32.2, pp. 113 – 130 . issn: 0144-8463 , 1573-4935. doi: 10.1042/BSR20110046 . OpenUrl CrossRef ↵ Borgo , Christian et al. (May 17 , 2021 ). “Protein kinase CK2: a potential therapeutic target for diverse human diseases” . In: Signal Transduction and Targeted Therapy 6.1, p. 183 . issn: 2059-3635 . doi: 10.1038/s41392-021-00567-7 . OpenUrl CrossRef Butler , Andrew et al. ( May 2018 ). “ Integrating single-cell transcriptomic data across different con- ditions, technologies, and species ”. In: Nature Biotechnology 36 . 5 , pp. 411 – 420 . issn: 1087-0156 , 1546-1696. doi: 10.1038/nbt.4096 . OpenUrl CrossRef PubMed ↵ Cairo , Stefano , et al. (Dec. 2008 ). “ Hepatic Stem-like Phenotype and Interplay of Wnt/ β -Catenin and Myc Signaling in Aggressive Childhood Liver Cancer ”. In: Cancer Cell 14 . 6 , pp. 471 – 484 . issn: 15356108 . doi: 10.1016/j.ccr.2008.11.002 . OpenUrl CrossRef PubMed Web of Science ↵ Carrillo-Reixach , Juan , et al. (Aug. 2020 ). “ Epigenetic footprint enables molecular risk stratification of hepatoblastoma with clinical implications ”. In: Journal of Hepatology 73 . 2 , pp. 328 – 341 . issn: 01688278 . doi: 10.1016/j.jhep.2020.03.025 . OpenUrl CrossRef PubMed ↵ Christiansen , Anders and Lars Dyrskjøt (Apr. 2013 ). “ The functional role of the novel biomarker karyopherin α 2 (KPNA2) in cancer ”. In: Cancer Letters 331 . 1 , pp. 18 – 23 . issn: 03043835 . doi: 10.1016/j.canlet.2012.12.013 . OpenUrl CrossRef PubMed Web of Science ↵ Cusanovich , Darren A. et al. (May 22 , 2015 ). “ Multiplex single-cell profiling of chromatin accessibility by combinatorial cellular indexing” . In: Science 348.6237, pp. 910 – 914 . issn: 0036-8075 , 1095-9203. doi: 10.1126/science.aab1601 . OpenUrl Abstract / FREE Full Text ↵ D’Urso , Agustina and Jason H. Brickner (June 2014 ). “ Mechanisms of epigenetic memory ”. In: Trends in Genetics 30 . 6 , pp. 230 – 236 . issn: 01689525 . doi: 10.1016/j.tig.2014.04.004 . OpenUrl CrossRef PubMed ↵ Da Silva-Diz , Victoria et al. (Dec. 2018 ). “ Cancer cell plasticity: Impact on tumor progression and therapy response ”. In: Seminars in Cancer Biology 53 , pp. 48 – 58 . issn: 1044579X . doi: 10.1016/j.semcancer.2018.08.009 . OpenUrl CrossRef Darbari, A (Sept . 2003 ). “ Epidemiology of primary hepatic malignancies in U.S. children ”. In: Hepa- tology 38 . 3 , pp. 560 – 566 . issn: 02709139 . doi: 10.1053/jhep.2003.50375 . OpenUrl CrossRef Dolgalev, Igor (Apr. 12 , 2018 ). msigdbr: MSigDB Gene Sets for Multiple Organisms in a Tidy Data Format . doi: 10.32614/CRAN.package.msigdbr . OpenUrl CrossRef ↵ Feng , Jincheng , et al. (Dec. 2019 ). “ Incidence trends and survival prediction of hepatoblastoma in children: a population-based study ”. In: Cancer Communications 39 . 1 , p. 62 . issn: 25233548 doi: 10.1186/s40880-019-0411-7 . OpenUrl CrossRef Gröbner, Susanne N., et al. (Mar. 15 , 2018 ). “The landscape of genomic alterations across child- hood cancers” . In: Nature 555.7696, pp. 321 – 327 . issn: 0028-0836 , 1476-4687. doi: 10.1038/nature25480 . OpenUrl CrossRef ↵ Hirsch , Theo Z. et al. (Oct. 1 , 2021 ). “Integrated Genomic Analysis Identifies Driver Genes and Cisplatin-Resistant Progenitor Phenotype in Pediatric Liver Cancer” . In: Cancer Discovery 11.10, pp. 2524 – 2543 . issn: 2159-8274 , 2159-8290. doi: 10.1158/2159-8290.CD-20-1809 . OpenUrl Abstract / FREE Full Text ↵ Hooks , Katarzyna B. et al. (July 2018 ). “ New insights into diagnosis and therapeutic options for proliferative hepatoblastoma ”. In: Hepatology 68 . 1 , pp. 89 – 102 . issn: 02709139 . doi: 10.1002/hep.29672 . OpenUrl CrossRef PubMed ↵ Huang , Hongting et al. (Oct. 12 , 2022 ). “Single-cell transcriptomics uncovers cellular architecture and developmental trajectories in hepatoblastoma” . In: Hepatology , hep.32775. issn: 0270-9139 , 1527-3350. doi: 10.1002/hep.32775 . OpenUrl CrossRef ↵ Hubbard , Aubrey K et al. (Mar. 1 , 2019 ). “Trends in International Incidence of Pediatric Cancers in Children Under 5 Years of Age: 1988–2012” . In: JNCI Cancer Spectrum 3.1, pkz007. issn: 2515-5091 . doi: 10.1093/jncics/pkz007 . OpenUrl CrossRef Ji , Zhicheng and Hongkai Ji (July 27, 2016 ). “TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis” . In: Nucleic Acids Research 44.13, e117 – e117 . issn: 0305-1048 , 1362-4962 . doi: 10.1093/nar/gkw430 . OpenUrl CrossRef PubMed ↵ Koch , A. , et al. (Jan. 15, 1999 ). “Childhood hepatoblastomas frequently carry a mutated degradation targeting box of the beta-catenin gene” . In : Cancer Research 59.2, pp. 269 – 273 . issn: 0008-5472 . Korsunsky , Ilya , et al. (Dec. 2019 ). “ Fast, sensitive and accurate integration of single-cell data with Harmony ”. In: Nature Methods 16 . 12 , pp. 1289 – 1296 . issn: 1548-7091 , 1548-7105. doi: 10.1038/s41592-019-0619-0 . OpenUrl CrossRef PubMed Lawrence , Michael et al. (Aug. 8 , 2013 ). “Software for Computing and Annotating Genomic Ranges” . In: PLoS Computational Biology 9.8. Ed. by Andreas Prlic , e1003118. issn: 1553-7358 . doi: 10.1371/journal.pcbi.1003118 . OpenUrl CrossRef PubMed ↵ Lee-Theilen , Mieun et al. (Nov. 25 , 2022 ). “Hepatoblastoma Cancer Stem Cells Express PD-L1, Reveal Plasticity and Can Emerge upon Chemotherapy” . In: Cancers 14.23, p. 5825 . issn: 2072-6694 . doi: 10.3390/cancers14235825 . OpenUrl CrossRef Lemaigre, Fŕedéric P . (July 2009 ). “ Mechanisms of Liver Development: Concepts for Understand- ing Liver Disorders and Design of Novel Therapies ”. In: Gastroenterology 137 . 1 , pp. 62 – 79 . issn: 00165085 . doi: 10.1053/j.gastro.2009.03.035 . OpenUrl CrossRef PubMed Web of Science ↵ Li , Fengzhi , Ieman Aljahdali , and Xiang Ling (Dec. 2019 ). “ Cancer therapeutics using survivin BIRC5 as a target: what can we do after over two decades of study? ” In: Journal of Experimental & Clinical Cancer Research 38 . 1 , p. 368 . issn: 1756-9966. doi: 10.1186/s13046-019-1362-1 . OpenUrl CrossRef PubMed Li , Heng and Richard Durbin (July 15, 2009 ). “Fast and accurate short read alignment with Bur- rows–Wheeler transform” . In: Bioinformatics 25 .14, pp. 1754 – 1760 . issn: 1367-4811 , 1367-4803. doi: 10.1093/bioinformatics/btp324 . OpenUrl CrossRef PubMed Web of Science Li , Heng , Bob Handsaker , et al. (Aug. 15 , 2009 ). “ The Sequence Alignment/Map format and SAM- tools” . In: Bioinformatics 25.16, pp. 2078 – 2079 . issn: 1367-4811 , 1367-4803. doi: 10.1093/bioinformatics/btp352 . OpenUrl CrossRef PubMed Web of Science Liao , Yang , Gordon K Smyth , and Wei Shi (May 7, 2019 ). “The R package Rsubread is easier, faster, cheaper and better for alignment and quantification of RNA sequencing reads” . In: Nucleic Acids Research 47.8 , e47 – e47 . issn: 0305-1048, 1362-4962 . doi: 10.1093/nar/gkz114 . OpenUrl CrossRef PubMed Lun , Aaron T. L. , Davis J. McCarthy , and John C. Marioni ( 2016 ). “ A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor ”. In: F1000Research 5, p. 2122 . issn: 2046-1402 . doi: 10.12688/f1000research.9501.2 . OpenUrl CrossRef PubMed ↵ Nagae , Genta , et al. (Dec. 2021 ). “ Genetic and epigenetic basis of hepatoblastoma diversity ”. In: Nature Communications 12 . 1 , p. 5423 . issn: 2041-1723 . doi: 10.1038/s41467-021-25430-9 . OpenUrl CrossRef PubMed ↵ Qin , Siyuan et al. (Oct. 7 , 2020 ). “Emerging role of tumor cell plasticity in modifying therapeutic response” . In: Signal Transduction and Targeted Therapy 5.1, p. 228 . issn: 2059-3635 . doi: 10.038/s41392-020-00313-5 . OpenUrl CrossRef ↵ Robichaux , Jacqulyne P. , et al. (Oct. 2019 ). “ Pan-Cancer Landscape and Analysis of ERBB2 Mutations Identifies Poziotinib as a Clinically Active Inhibitor and Enhancer of T-DM1 Activity ”. In: Cancer Cell 36 . 4 , 444 – 457 .e7. issn: 15356108. doi: 10.1016/j.ccell.2019.09.001 . OpenUrl CrossRef PubMed Roehrig, Amélie, et al. (Apr. 8 , 2024 ). “ Single-cell multiomics reveals the interplay of clonal evolution and cellular plasticity in hepatoblastoma ”. In: Nature Communications 15.1, p. 3031 . issn: 2041-1723 . doi: 10.1038/s41467-024-47280-x . OpenUrl CrossRef Sara Aibar , Stein Aerts . Laboratory Of Computational Biology. VIB-KU Leuven Center For Brain ( 2018 ). RcisTarget . doi: 10.18129/B9.BIOC.RCISTARGET . OpenUrl CrossRef Satpathy , Ansuman T. , et al. (Aug. 2019 ). “ Massively parallel single-cell chromatin landscapes of human immune cell development and intratumoral T cell exhaustion ”. In: Nature Biotechnology 37 . 8 , pp. 925 – 936 . issn: 1087-0156 , 1546-1696. doi: 10.1038/s41587-019-0206-z . OpenUrl CrossRef PubMed ↵ Schlichting , Carl D and Massimo Pigliucci ( 1998 ). Phenotypic evolution: a reaction norm perspective. Sekiguchi, Masahiro et al. (July 7, 2020). “Integrated multiomics analysis of hepatoblastoma unravels its heterogeneity and provides novel druggable targets” . In: npj Precision Oncology 4.1, p. 20 . issn: 2397-768X . doi: 10.1038/s41698-020-0125-y . OpenUrl CrossRef ↵ Shen , Ding-Wu et al. (July 2012 ). “ Cisplatin Resistance: A Cellular Self-Defense Mechanism Resulting from Multiple Epigenetic and Genetic Changes ”. In: Pharmacological Reviews 64 . 3 , pp. 706 – 721 . issn: 00316997. doi: 10.1124/pr.111.005637 . OpenUrl Abstract / FREE Full Text ↵ Song , Hanbing et al. (Aug. 25 , 2022 ). “Single-cell analysis of hepatoblastoma identifies tumor signa- tures that predict chemotherapy susceptibility using patient-specific tumor spheroids” . In: Nature Communications 13.1, p. 4878. issn: 2041-1723 . doi: 10.1038/s41467-022-32473-z . OpenUrl CrossRef PubMed ↵ Street , Kelly , et al. (Dec. 2018 ). “ Slingshot: cell lineage and pseudotime inference for single-cell tran- scriptomics ”. In: BMC Genomics 19 . 1 , p. 477 . issn: 1471-2164. doi: 10.1186/s12864-018-4772-0 . OpenUrl CrossRef PubMed Struyf , Anja , Mia Hubert , and Peter Rousseeuw ( 1996 ). “ Clustering in an Object-Oriented Environ- ment ”. In: Journal of Statistical Software 1 . 4 . issn: 1548-7660 . doi: 10.18637/jss.v001.i04 . OpenUrl CrossRef Stuart , Tim , et al. (Nov. 2021 ). “ Single-cell chromatin state analysis with Signac ”. In: Nature Methods 18 . 11 , pp. 1333 – 1341 . issn: 1548-7091, 1548-7105 . doi: 10.1038/s41592-021-01282-5 . OpenUrl CrossRef PubMed ↵ Sumazin , Pavel , et al. (Oct. 2022 ). “ Hepatoblastomas with carcinoma features represent a biological spectrum of aggressive neoplasms in children and young adults ”. In: Journal of Hepatology 77 . 4 , pp. 1026 – 1037 . issn: 01688278. doi: 10.1016/j.jhep.2022.04.035 . OpenUrl CrossRef PubMed ↵ Si-Tayeb , Karim , Frédéric P. Lemaigre , and Stephen A. Duncan (Feb. 2010 ). “ Organogenesis and Development of the Liver ”. In: Developmental Cell 18 . 2 , pp. 175 – 189 . issn: 15345807 . doi: 10.1016/j.devcel.2010.01.011 . OpenUrl CrossRef PubMed Web of Science ↵ Wang , Kun et al. (June 2025 ). “ Incidence trends and a nomogram for predicting overall survival in children with hepatoblastoma: A population-based analysis ”. In: European Journal of Surgical Oncology 51 . 6 , p. 109694 . issn: 07487983 . doi: 10.1016/j.ejso.2025.109694 . OpenUrl CrossRef ↵ West-Eberhard , Mary Jane ( 2003 ). Developmental plasticity and evolution . Oxford University Press . ↵ Whiting , Frederick J.H. et al. (June 2024 ). “Phenotypic noise and plasticity in cancer evolution” . In: Trends in Cell Biology 34.6 , pp. 451 –464. issn: 09628924 . doi: 10.1016/j.tcb.2023.10.002 . OpenUrl CrossRef Yu , Guangchuang et al. ( May 2012 ). “ clusterProfiler: an R Package for Comparing Biological Themes Among Gene Clusters ”. In: OMICS: A Journal of Integrative Biology 16 . 5 , pp. 284 – 287 . issn: 1536-2310, 1557-8100 . doi: 10.1089/omi.2011.0118 . OpenUrl CrossRef PubMed Web of Science Zhang , Yong et al. (Sept. 17 , 2008 ). “Model-based Analysis of ChIP-Seq (MACS)” . In: Genome Biology 9.9, R137. issn: 1474-760X . doi: 10.1186/gb-2008-9-9-r137 . OpenUrl CrossRef PubMed Zheng , Grace X. Y. et al. (Jan. 16, 2017 ). “Massively parallel digital transcriptional profiling of single cells” . In: Nature Communications 8.1 , p. 14049 . issn: 2041-1723 . doi: 10.1038/ncomms14049 . OpenUrl CrossRef ↵ Zong , Yiwei and Ben Z. Stanger (Sept. 2012 ). “ Molecular mechanisms of liver and bile duct devel- opment ”. In: WIREs Developmental Biology 1 . 5 , pp. 643 – 655 . issn: 1759-7684 , 1759-7692 . doi: 10.1002/wdev.47 . OpenUrl CrossRef View the discussion thread. Back to top Previous Next Posted April 18, 2025. Download PDF Supplementary Material Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following The Role of Phenotypic Plasticity in Adaptation to Treatment and Prospective Plasticity Drivers in Hepatoblastoma Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. 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