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• W3099741968 abstract "•Spiked-scRNA-seq assigns functional phenotypes to single-cell RNA-seq clusters•Intra-tumoral pro-metastatic heterogeneity emerges from cell-cell interactions•VSIG1 represses pro-metastatic states cell autonomously and non-cell autonomously•The Ig-domain transmembrane protein VSIG1 inhibits YAP/TAZ-TEAD signaling How cells with metastatic potential, or pro-metastatic states, arise within heterogeneous primary tumors remains unclear. Here, we have used one index primary colon cancer to develop spiked-scRNAseq to link omics-defined single-cell clusters with cell behavior. Using spiked-scRNAseq we uncover cell populations with differential metastatic potential in which pro-metastatic states are correlated with the expression of signaling and vesicle-trafficking genes. Analyzing such heterogeneity, we define an anti-metastatic, non-cell-autonomous interaction originating from non-/low-metastatic cells, and identify membrane VSIG1 as a critical mediator of this interaction. VSIG1 acts to restrict the development of pro-metastatic states autonomously and non-cell autonomously, in part by inhibiting YAP/TAZ-TEAD signaling. As VSIG1 re-expression is able to reduce metastatic behavior from multiple colon cancer cell types, the regulation of VSIG1 or its effectors opens new interventional opportunities. In general, we propose that crosstalk between cancer cells, including the action of VSIG1, dynamically defines the degree of pro-metastatic intra-tumoral heterogeneity. How cells with metastatic potential, or pro-metastatic states, arise within heterogeneous primary tumors remains unclear. Here, we have used one index primary colon cancer to develop spiked-scRNAseq to link omics-defined single-cell clusters with cell behavior. Using spiked-scRNAseq we uncover cell populations with differential metastatic potential in which pro-metastatic states are correlated with the expression of signaling and vesicle-trafficking genes. Analyzing such heterogeneity, we define an anti-metastatic, non-cell-autonomous interaction originating from non-/low-metastatic cells, and identify membrane VSIG1 as a critical mediator of this interaction. VSIG1 acts to restrict the development of pro-metastatic states autonomously and non-cell autonomously, in part by inhibiting YAP/TAZ-TEAD signaling. As VSIG1 re-expression is able to reduce metastatic behavior from multiple colon cancer cell types, the regulation of VSIG1 or its effectors opens new interventional opportunities. In general, we propose that crosstalk between cancer cells, including the action of VSIG1, dynamically defines the degree of pro-metastatic intra-tumoral heterogeneity. Defining and understanding the role of heterogeneity in complex cellular systems, such as a growing tumor, are central questions in biology and medicine. For instance, how some cells but not others chose metastatic fates within primary carcinomas is not known (e.g., Lambert et al., 2017Lambert A.W. Pattabiraman D.R. Weinberg R.A. Emerging Biological Principles of Metastasis.Cell. 2017; 168: 670-691Abstract Full Text Full Text PDF PubMed Scopus (1610) Google Scholar). Recent studies support neither a straight clonal division of pro- and anti-metastatic fates nor the existence of common metastatic driver mutations distinct from those that drive tumor progression, although genetic selection of clones and genetic drift have been documented (Jones et al., 2008Jones S. Chen W.D. Parmigiani G. Diehl F. Beerenwinkel N. Antal T. Traulsen A. Nowak M.A. Siegel C. Velculescu V.E. et al.Comparative lesion sequencing provides insights into tumor evolution.Proc. Natl. Acad. Sci. USA. 2008; 105: 4283-4288Crossref PubMed Scopus (630) Google Scholar; Gupta and Somer, 2017Gupta R.G. Somer R.A. Intratumor Heterogeneity: Novel Approaches for Resolving Genomic Architecture and Clonal Evolution.Mol. Cancer Res. 2017; 15: 1127-1137Crossref PubMed Scopus (36) Google Scholar; Turajlic et al., 2019Turajlic S. Sottoriva A. Graham T. Swanton C. Resolving genetic heterogeneity in cancer.Nat. Rev. Genet. 2019; 20: 404-416Crossref PubMed Scopus (291) Google Scholar; Hu et al., 2019Hu Z. Ding J. Ma Z. Sun R. Seoane J.A. Scott Shaffer J. Suarez C.J. Berghoff A.S. Cremolini C. Falcone A. et al.Quantitative evidence for early metastatic seeding in colorectal cancer.Nat. Genet. 2019; 51: 1113-1122Crossref PubMed Scopus (218) Google Scholar; Priestley et al., 2019Priestley P. Baber J. Lolkema M.P. Steeghs N. de Bruijn E. Shale C. Duyvesteyn K. Haidari S. van Hoeck A. Onstenk W. et al.Pan-cancer whole-genome analyses of metastatic solid tumours.Nature. 2019; 575: 210-216Crossref PubMed Scopus (378) Google Scholar). Instead, metastasis may depend on intra-tumoral heterogeneity (Patel and Vanharanta, 2017Patel S.A. Vanharanta S. Epigenetic determinants of metastasis.Mol. Oncol. 2017; 11: 79-96Crossref PubMed Scopus (40) Google Scholar; Hu et al., 2017Hu Z. Sun R. Curtis C. A population genetics perspective on the determinants of intra-tumor heterogeneity.Biochim. Biophys. Acta Rev. Cancer. 2017; 1867: 109-126Crossref PubMed Scopus (34) Google Scholar; Iacobuzio-Donahue et al., 2020Iacobuzio-Donahue C.A. Litchfield K. Swanton C. Intratumor heterogeneity reflects clinical disease course.Nat. Can. 2020; 1: 3-6Crossref Scopus (27) Google Scholar). Previous studies have addressed this issue through transcriptional analyses of single cells or combined omics of organoids from colon cancers (Dalerba et al., 2011Dalerba P. Kalisky T. Sahoo D. Rajendran P.S. Rothenberg M.E. Leyrat A.A. Sim S. Okamoto J. Johnston D.M. Qian D. et al.Single-cell dissection of transcriptional heterogeneity in human colon tumors.Nat. Biotechnol. 2011; 29: 1120-1127Crossref PubMed Scopus (540) Google Scholar; Roerink et al., 2018Roerink S.F. Sasaki N. Lee-Six H. Young M.D. Alexandrov L.B. Behjati S. Mitchell T.J. Grossmann S. Lightfoot H. Egan D.A. et al.Intra-tumour diversification in colorectal cancer at the single-cell level.Nature. 2018; 556: 457-462Crossref PubMed Scopus (297) Google Scholar) and the sampling of multiple sites from individual tumors (e.g., Gerlinger et al., 2012Gerlinger M. Rowan A.J. Horswell S. Math M. Larkin J. Endesfelder D. Gronroos E. Martinez P. Matthews N. Stewart A. et al.Intratumor heterogeneity and branched evolution revealed by multiregion sequencing.N. Engl. J. Med. 2012; 366: 883-892Crossref PubMed Scopus (5750) Google Scholar; Casasent et al., 2018Casasent A.K. Schalck A. Gao R. Sei E. Long A. Pangburn W. Casasent T. Meric-Bernstam F. Edgerton M.E. Navin N.E. Multiclonal Invasion in Breast Tumors Identified by Topographic Single Cell Sequencing.Cell. 2018; 172: 205-217.e12Abstract Full Text Full Text PDF PubMed Scopus (224) Google Scholar). In this context, metastatic states could depend on epigenetic events and/or cell-to-cell signaling. Support for this idea includes the balance of anti- and pro-metastatic states via the modulation of protein secretion (Duquet et al., 2014Duquet A. Melotti A. Mishra S. Malerba M. Seth C. Conod A. Ruiz i Altaba A. A novel genome-wide in vivo screen for metastatic suppressors in human colon cancer identifies the positive WNT-TCF pathway modulators TMED3 and SOX12.EMBO Mol. Med. 2014; 6: 882-901Crossref PubMed Scopus (58) Google Scholar; Mishra et al., 2019Mishra S. Bernal C. Silvano M. Anand S. Ruiz i Altaba A. The protein secretion modulator TMED9 drives CNIH4/TGFα/GLI signaling opposing TMED3-WNT-TCF to promote colon cancer metastases.Oncogene. 2019; 38: 5817-5837Crossref PubMed Scopus (24) Google Scholar) and the existence of positive interactions that affect metastatic growth, multiclonal invasion, and clinical behavior in various cancers (Miller, 1983Miller F.R. Tumor subpopulation interactions in metastasis.Invasion Metastasis. 1983; 3: 234-242PubMed Google Scholar; Inda et al., 2010Inda M.M. Bonavia R. Mukasa A. Narita Y. Sah D.W. Vandenberg S. Brennan C. Johns T.G. Bachoo R. Hadwiger P. et al.Tumor heterogeneity is an active process maintained by a mutant EGFR-induced cytokine circuit in glioblastoma.Genes Dev. 2010; 24: 1731-1745Crossref PubMed Scopus (405) Google Scholar; Calbo et al., 2011Calbo J. van Montfort E. Proost N. van Drunen E. Beverloo H.B. Meuwissen R. Berns A. A functional role for tumor cell heterogeneity in a mouse model of small cell lung cancer.Cancer Cell. 2011; 19: 244-256Abstract Full Text Full Text PDF PubMed Scopus (253) Google Scholar; Celià-Terrassa et al., 2012Celià-Terrassa T. Meca-Cortés O. Mateo F. Martínez de Paz A. Rubio N. Arnal-Estapé A. Ell B.J. Bermudo R. Díaz A. Guerra-Rebollo M. et al.Epithelial-mesenchymal transition can suppress major attributes of human epithelial tumor-initiating cells.J. Clin. Invest. 2012; 122: 1849-1868Crossref PubMed Scopus (357) Google Scholar; Cleary et al., 2014Cleary A.S. Leonard T.L. Gestl S.A. Gunther E.J. Tumour cell heterogeneity maintained by cooperating subclones in Wnt-driven mammary cancers.Nature. 2014; 508: 113-117Crossref PubMed Scopus (328) Google Scholar; Marusyk et al., 2014Marusyk A. Tabassum D.P. Altrock P.M. Almendro V. Michor F. Polyak K. Non-cell-autonomous driving of tumour growth supports sub-clonal heterogeneity.Nature. 2014; 514: 54-58Crossref PubMed Scopus (417) Google Scholar; Vinci et al., 2018Vinci M. Burford A. Molinari V. Kessler K. Popov S. Clarke M. Taylor K.R. Pemberton H.N. Lord C.J. Gutteridge A. et al.Functional diversity and cooperativity between subclonal populations of pediatric glioblastoma and diffuse intrinsic pontine glioma cells.Nat. Med. 2018; 24: 1204-1215Crossref PubMed Scopus (98) Google Scholar). However, since colon cancer heterogeneity is reestablished in single-cell clonal grafts (Dalerba et al., 2011Dalerba P. Kalisky T. Sahoo D. Rajendran P.S. Rothenberg M.E. Leyrat A.A. Sim S. Okamoto J. Johnston D.M. Qian D. et al.Single-cell dissection of transcriptional heterogeneity in human colon tumors.Nat. Biotechnol. 2011; 29: 1120-1127Crossref PubMed Scopus (540) Google Scholar), and dominant positive interactions would tend to homogenize the outcome, these may be secondary to the initial establishment of pro-metastatic states. Many of these studies rely on population assays of primary tumors, and therefore it remained unclear whether there could be operationally defined heterogeneity of metastatic potential (hereafter called pro-metastatic heterogeneity) in primary tumors at the single-cell level, and what kinds of cellular interactions may shape the choice of a pro-metastatic state. Single-cell RNA sequencing (scRNAseq) analyses have described the existence of different clusters within primary tumors, although how these may relate to actual behaviors remains unknown (e.g., Li et al., 2017Li H. Courtois E.T. Sengupta D. Tan Y. Chen K.H. Goh J.J.L. Kong S.L. Chua C. Hon L.K. Tan W.S. et al.Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors.Nat. Genet. 2017; 49: 708-718Crossref PubMed Scopus (555) Google Scholar; Kotliar et al., 2019Kotliar D. Veres A. Nagy M.A. Tabrizi S. Hodis E. Melton D.A. Sabeti P.C. Identifying gene expression programs of cell-type identity and cellular activity with single-cell RNA-Seq.eLife. 2019; 8: e43803Crossref PubMed Scopus (91) Google Scholar). Functional tests with a heterogeneous population cannot ascribe a particular role to tumor subpopulations. Assigning functional properties to individual scRNAseq clusters is a major challenge in the field (Lawson et al., 2018Lawson D.A. Kessenbrock K. Davis R.T. Pervolarakis N. Werb Z. Tumour heterogeneity and metastasis at single-cell resolution.Nat. Cell Biol. 2018; 20: 1349-1360Crossref PubMed Scopus (285) Google Scholar). Single-cell clones were made from the heterogeneous primary CC14 colon cancer cell population (Varnat et al., 2009Varnat F. Duquet A. Malerba M. Zbinden M. Mas C. Gervaz P. Ruiz i Altaba A. Human colon cancer epithelial cells harbour active HEDGEHOG-GLI signalling that is essential for tumour growth, recurrence, metastasis and stem cell survival and expansion.EMBO Mol. Med. 2009; 1: 338-351Crossref PubMed Scopus (383) Google Scholar) and challenged for transfilter migratory behavior in vitro (Figure 1A). Low/non- (C11) and high-migratory (E3) clones showed a stable >10-fold difference (Figures 1A and S1A), although they displayed similar morphology, and percentages of CD133+ and spheroid-forming cells (Figures S1B and S1C; data not shown). Subcutaneous xenografts of lacZ+ E3 and C11 yielded similar tumor volumes. However, E3lacZ produced 10-fold more lung metastases than C11lacZ (Figures 1B and 1C). Given these congruent results (see also Repesh, 1989Repesh L.A. A new in vitro assay for quantitating tumor cell invasion.Invasion Metastasis. 1989; 9: 192-208PubMed Google Scholar; Mishra et al., 2019Mishra S. Bernal C. Silvano M. Anand S. Ruiz i Altaba A. The protein secretion modulator TMED9 drives CNIH4/TGFα/GLI signaling opposing TMED3-WNT-TCF to promote colon cancer metastases.Oncogene. 2019; 38: 5817-5837Crossref PubMed Scopus (24) Google Scholar), we hereafter describe such phenotypes as metastatic behavior. Analyses of additional high- (E6 lacZ), medium- (B2 lacZ), and low-/non- (F3 lacZ) metastatic clones reproduced these results (Figures 1A, 1B, and S1A), and single-cell subcloning of C11 confirmed the stability of the phenotype (Figure S1D). Exome sequencing of E3 and C11 revealed shared early and late driver mutations (e.g., APC, SMAD4), suggesting their late split (Gerstung et al., 2020Gerstung M. Jolly C. Leshchiner I. Dentro S.C. Gonzalez S. Rosebrock D. Mitchell T.J. Rubanova Y. Anur P. Yu K. et al.PCAWG Evolution & Heterogeneity Working GroupPCAWG ConsortiumThe evolutionary history of 2,658 cancers.Nature. 2020; 578: 122-128Crossref PubMed Scopus (380) Google Scholar). Only 3.5% of CC14-specific SNPs different from the reference genome were differentially segregated in a heterozygous manner in E3 versus C11 (Figures 1D and S1E). As no alleles were detected in CC14 (with frequencies >0.5%) absent from E3 or C11, and these clones did not exhibit significant levels of SNPs not found in the parental mixture, they represent the allelic variation of the parental population. Whereas two coding and four STOP SNPs distributed in E3 versus C11 separated high-/medium- (E3, E6, and B2) versus low-metastatic (C11 and F3) clones (Figures 1E and S1F; data not shown), they cannot account for the intermediate degree of metastatic activity (e.g., B2 versus E3 and E6), suggesting a separation of genetic determinants and metastatic behavior. Enrichment and predicted-protein interaction software analyses of clone transcriptomic data highlighted changes in extracellular remodeling, inflammatory responses, membrane-bound organelles, and signaling components, including cytokines, in E3/E6 high- versus C11/F3 low-/non-metastatic clones. Upregulated genes included those involved in signaling, cell adhesion, vesicular trafficking (e.g., CAV1), and transcription (some families were clustered: e.g., MAGEAs in chX and ZNFs in ch19), whereas downregulated genes highlighted the Golgi (Figures 1F, S1G, and S1H). Moreover, the transcriptome of B2 cells allowed the classification of gene expression changes in relation to graded metastatic behavior (Figure 1G; Table S1). scRNAseq analyses of CC14 yielded 2 main clusters with cell-cycle phases (Figure S1I) and 2 small side populations (SPs) in G1 (Figure 2A and Table S2). To assign metastatic phenotypes to single-cell clusters, we included ∼100 cells of each of the E3GFP, C11puromycin, and B2lacZ clones as tracing spikes into the bulk CC14 population. After sequencing, spiked cells identified by their stable genetic lentiviral tags (GFP, puromycin, or lacZ) were mapped to different clusters (Figure 2B). High E3GFP and medium-metastatic B2lacZ cells homed to the smaller cluster, hereafter called metastatic CL (METCL), and low-/non-metastatic C11puromycin cells homed to the larger cluster, hereafter called NONMETCL. The relative sizes of these clusters approximated the distribution of C11 and E3-specific SNPs. The SP of the NONMETCL, hereafter called NONMETSP, was characterized by the high level and frequency of cells expressing SPINK4, TFF3, and other genes (Figures 2C–2E and S2) that identified it as comprising goblet-like secretory cells (Dalerba et al., 2011Dalerba P. Kalisky T. Sahoo D. Rajendran P.S. Rothenberg M.E. Leyrat A.A. Sim S. Okamoto J. Johnston D.M. Qian D. et al.Single-cell dissection of transcriptional heterogeneity in human colon tumors.Nat. Biotechnol. 2011; 29: 1120-1127Crossref PubMed Scopus (540) Google Scholar). The METSP, in contrast, showed a high enrichment for XBP1-regulated genes over the NONMETSP (Figures 2D, S2, and S3A), suggesting endoplasmic reticulum (ER) stress responses. It also contained a predicted network of ER-Golgi protein trafficking, including TMED9 (Figures 2D and S2), which encodes a protein secretion cargo selector implicated in pro-metastatic states (Mishra et al., 2019Mishra S. Bernal C. Silvano M. Anand S. Ruiz i Altaba A. The protein secretion modulator TMED9 drives CNIH4/TGFα/GLI signaling opposing TMED3-WNT-TCF to promote colon cancer metastases.Oncogene. 2019; 38: 5817-5837Crossref PubMed Scopus (24) Google Scholar). Given that a number of secretory markers were detected in both SPs (Figure S3C), the expression of a 486-strong secretory cohort (Feizi et al., 2017Feizi A. Gatto F. Uhlen M. Nielsen J. Human protein secretory pathway genes are expressed in a tissue-specific pattern to match processing demands of the secretome.NPJ Syst. Biol. Appl. 2017; 3: 22Crossref PubMed Scopus (19) Google Scholar) (Table S3) was analyzed, revealing marked differences between the SPs (Figures S3D and S3E), and suggesting the secretory but non-Goblet-like nature of METSP cells. scRNAseq mapping of CC36, a KRASG13D colon cancer primary culture (Varnat et al., 2009Varnat F. Duquet A. Malerba M. Zbinden M. Mas C. Gervaz P. Ruiz i Altaba A. Human colon cancer epithelial cells harbour active HEDGEHOG-GLI signalling that is essential for tumour growth, recurrence, metastasis and stem cell survival and expansion.EMBO Mol. Med. 2009; 1: 338-351Crossref PubMed Scopus (383) Google Scholar), revealed two clusters using principal-component analysis (PCA) dimensions 3–10, including the prominent inverse expression of EREG and BMP4 (Figures 2F and S4A–S4C; Table S4). Single-cell clones from the parental population were made and tested in transfilter assays, showing two main groups with low- versus high-metastatic behaviors (Figure 2G). High-metastatic clone 29 (CL29lacZ) and low-metastatic clone 1 (CL1lacZ) reproduced their different behavior in vivo xenografts scoring for distant metastases (Figure 2H). To match behavior to transcriptomic profiles, instead of spiking, here, we directly compared bulk clone transcriptomes and scRNAseq data using a cutoff of detected expression in >10% of the cells analyzed. Genes that mapped to the EREG cluster were upregulated in the transcriptome of low-metastatic CL1 versus high-metastatic CL29 cells, and conversely, genes that mapped to the BMP4 cluster were upregulated in CL29 versus CL1 cells (Figures 2F, 2I, and S4C; Table S4). Critically, there was no cross-matching of genes (Figure 2I). The partial overlap between the sets likely derives from the relatively low depth of the scRNAseq data. The differential expression of genes marking the EREGhigh (LOWMETCL) versus BMP4high (HIGHMETCL) cluster genes was reproduced in a second pair of low-metastatic (CL28) and high-metastatic (CL6) CC36 clones (Figure S4D). Testing the participation of these markers in metastatic behavior revealed that BMP4 but not EREG promoted cell migration (Figures S4E and S4F), consistent with previous data (Lorente-Trigos et al., 2010Lorente-Trigos A. Varnat F. Melotti A. Ruiz i Altaba A. BMP signaling promotes the growth of primary human colon carcinomas in vivo.J. Mol. Cell Biol. 2010; 2: 318-332Crossref PubMed Scopus (37) Google Scholar; see also Voorneveld et al., 2014Voorneveld P.W. Kodach L.L. Jacobs R.J. Liv N. Zonnevylle A.C. Hoogenboom J.P. Biemond I. Verspaget H.W. Hommes D.W. de Rooij K. et al.Loss of SMAD4 alters BMP signaling to promote colorectal cancer cell metastasis via activation of Rho and ROCK.Gastroenterology. 2014; 147: 196-208.e13Abstract Full Text Full Text PDF PubMed Scopus (127) Google Scholar). Analyses of predicted protein interactions using CL29 versus CL1 transcriptomes highlighted upregulated transport/localization/secretion, vesicle-mediated transport, lipid metabolism, versus downregulated cell-cycle parameters, response to stress, and microtubule/cytoskeleton activity (Figure S4G). It is paradoxical that CC14 high-metastatic cells display high levels of specific vesicle trafficking markers, whereas these are notable in the lower metastatic cluster of CC36. Although CAV1, for instance, can have context-dependent functions (Sloan et al., 2004Sloan E.K. Stanley K.L. Anderson R.L. Caveolin-1 inhibits breast cancer growth and metastasis.Oncogene. 2004; 23: 7893-7897Crossref PubMed Scopus (139) Google Scholar; Zhang et al., 2019Zhang C. Huang H. Zhang J. Wu Q. Chen X. Huang T. Li W. Liu Y. Zhang J. Caveolin-1 promotes invasion and metastasis by upregulating Pofut1 expression in mouse hepatocellular carcinoma.Cell Death Dis. 2019; 10: 477Crossref PubMed Scopus (17) Google Scholar, Campos et al., 2019Campos A. Burgos-Ravanal R. González M.F. Huilcaman R. Lobos González L. Quest A.F.G. Cell Intrinsic and Extrinsic Mechanisms of Caveolin-1-Enhanced Metastasis.Biomolecules. 2019; 9: E314Crossref PubMed Scopus (24) Google Scholar), it is possible that instead of specific genes, it is the processes in which they participate, such as secretion, vesicle trafficking, and signaling, that mark pro-metastatic cells. To test the match between high-metastatic gene signatures with migratory behavior, we used CC14 cells that had actively migrated in transfilter assays (Figures 3A and 3B ). Most of the MET genes tested showed higher or similar expression ratios in the migratory over the control non-migrated population. In contrast, all CC14 NONMETCL/SP markers were strongly underrepresented (Figure 3B). Moreover, the CC36 HIGHMET markers BMP4 and MSX1 were highly expressed in migrating CC14 cells (Figure 3B). Of the most highly expressed genes in migrating cells, CAV1 was also strongly differentially expressed in high- versus low-metastatic cells (Figure 1F). We thus tested the role of this gene as a representative of the vesicular trafficking gene cohort: CAV1 knockdown (KD) (using a validated small hairpin RNA [shRNA]; Rausch et al., 2019Rausch V. Bostrom J.R. Park J. Bravo I.R. Feng Y. Hay D.C. Link B.A. Hansen C.G. The Hippo Pathway Regulates Caveolae Expression and Mediates Flow Response via Caveolae.Curr. Biol. 2019; 29: 242-255.e6Abstract Full Text Full Text PDF PubMed Scopus (43) Google Scholar) in E3 cells resulted in a reduction in transfilter migration by nearly 80% (Figure 3C), paralleling its involvement in other cases (Zhang et al., 2019Zhang C. Huang H. Zhang J. Wu Q. Chen X. Huang T. Li W. Liu Y. Zhang J. Caveolin-1 promotes invasion and metastasis by upregulating Pofut1 expression in mouse hepatocellular carcinoma.Cell Death Dis. 2019; 10: 477Crossref PubMed Scopus (17) Google Scholar, but see also Sloan et al., 2004Sloan E.K. Stanley K.L. Anderson R.L. Caveolin-1 inhibits breast cancer growth and metastasis.Oncogene. 2004; 23: 7893-7897Crossref PubMed Scopus (139) Google Scholar). We next asked whether spiked-scRNAseq could be used to ascertain differential drug effects on cell subpopulations. As a proof of principle, we chose the BRAF inhibitor vemurafenib, as all CC14 cells carry the BRAFV600E mutation. Strikingly, the majority of METSP markers tested were highly upregulated after 48 h of treatment (Figure 3D), raising the possibility that although we found that BRAF inhibition halves cell numbers (data not shown), it could actually promote metastatic behavior in surviving cells, perhaps in line with its very poor efficacy in the clinic for this indication (Prahallad et al., 2012Prahallad A. Sun C. Huang S. Di Nicolantonio F. Salazar R. Zecchin D. Beijersbergen R.L. Bardelli A. Bernards R. Unresponsiveness of colon cancer to BRAF(V600E) inhibition through feedback activation of EGFR.Nature. 2012; 483: 100-103Crossref PubMed Scopus (1493) Google Scholar). Consistently, transfilter assays revealed that vemurafenib-treated CC14 cells were 2-fold more migratory than untreated controls (Figure 3D). As the transfilter efficiencies of the heterogenous parental primary populations lie in the middle range of those of the derived clones (Figures 1A and 2G), we tested the possibility that the frequency of pro-metastatic states in a heterogenous cell mixture depends on non-cell-autonomous influences. High-metastatic CC14 E3lacZ cells mixed at a 1:1 ratio with unmodified low-metastatic C11 (C11white) cells, yielded 50% fewer migratory lacZ+ cells in transfilter assays than the control E3lacZ/E3white mix (Figure 3E). Similarly, E3AP mixed with F3 lacZ cells yielded a 50% reduction in migrated AP+ cells (Figure 3E), which was recapitulated by the effect of C11white or F3 lacZ on E6AP cells (Figure 3E). Inversely, high-metastatic E3white did not significantly affect the migratory levels of C11lacZ or B2APcells (Figures 3E and S5A). Conditioned media were ineffective (Figures S5B–S5E). In vivo, distant metastases from tumors formed from a 1:1 mixture of E3lacZ and C11white were 3 times less abundant (after normalization by the ratio of lacZ+ cells in the tumor) than those derived from tumors from a similar E3lacZ/E3white mixture (Figure 3F). Analyses with CC36 CL1 and CL29 cells did not reveal a significant negative interaction. However, the highly metastatic CL29 cells prompted CL1 cells to become more metastatic, which was recapitulated by its conditioned media (Figures 3G, S5F, and S5G), paralleling previously data (Miller, 1983Miller F.R. Tumor subpopulation interactions in metastasis.Invasion Metastasis. 1983; 3: 234-242PubMed Google Scholar; Inda et al., 2010Inda M.M. Bonavia R. Mukasa A. Narita Y. Sah D.W. Vandenberg S. Brennan C. Johns T.G. Bachoo R. Hadwiger P. et al.Tumor heterogeneity is an active process maintained by a mutant EGFR-induced cytokine circuit in glioblastoma.Genes Dev. 2010; 24: 1731-1745Crossref PubMed Scopus (405) Google Scholar; Calbo et al., 2011Calbo J. van Montfort E. Proost N. van Drunen E. Beverloo H.B. Meuwissen R. Berns A. A functional role for tumor cell heterogeneity in a mouse model of small cell lung cancer.Cancer Cell. 2011; 19: 244-256Abstract Full Text Full Text PDF PubMed Scopus (253) Google Scholar; Celià-Terrassa et al., 2012Celià-Terrassa T. Meca-Cortés O. Mateo F. Martínez de Paz A. Rubio N. Arnal-Estapé A. Ell B.J. Bermudo R. Díaz A. Guerra-Rebollo M. et al.Epithelial-mesenchymal transition can suppress major attributes of human epithelial tumor-initiating cells.J. Clin. Invest. 2012; 122: 1849-1868Crossref PubMed Scopus (357) Google Scholar; Cleary et al., 2014Cleary A.S. Leonard T.L. Gestl S.A. Gunther E.J. Tumour cell heterogeneity maintained by cooperating subclones in Wnt-driven mammary cancers.Nature. 2014; 508: 113-117Crossref PubMed Scopus (328) Google Scholar; Marusyk et al., 2014Marusyk A. Tabassum D.P. Altrock P.M. Almendro V. Michor F. Polyak K. Non-cell-autonomous driving of tumour growth supports sub-clonal heterogeneity.Nature. 2014; 514: 54-58Crossref PubMed Scopus (417) Google Scholar; Vinci et al., 2018Vinci M. Burford A. Molinari V. Kessler K. Popov S. Clarke M. Taylor K.R. Pemberton H.N. Lord C.J. Gutteridge A. et al.Functional diversity and cooperativity between subclonal populations of pediatric glioblastoma and diffuse intrinsic pontine glioma cells.Nat. Med. 2018; 24: 1204-1215Crossref PubMed Scopus (98) Google Scholar). Thus, beyond previously described positive influences, the present results reveal critical negative cell-cell interactions, which may be mediated by membrane components. To identify candidates mediating the negative non-cell-autonomous effect, we compared genes differentially expressed (>5-fold) in C11 versus E3 and in F3 versus E6 clones, upregulated in the NONMETCL versus the METCL, and absent in CC36, where such negative interaction is not detected (Figure 3G). Four genes satisfied the criteria but only one encoded a transmembrane protein: v-set and immunoglobulin domain-containing protein 1 (VSIG1, Figures 3H and 3I; Scanlan et al., 2006Scanlan M.J. Ritter G. Yin B.W. Williams Jr., C. Cohen L.S. Coplan K.A. Fortunato S.R. Frosina D. Lee S.Y. Murray A.E. et al.Glycoprotein A34, a novel target for antibody-based cancer immunotherapy.Cancer Immun. 2006; 6: 2PubMed Google Scholar). Mapping VSIG1 onto the CC14 scRNAseq plot showed a <10% scattering of positive cells restricted to the NONMETCL (Figure 3J). The choice of VSIG1 was reinforced by previous findings with clinical relevance showing that (1) VSIG1 is heterogeneously expressed in normal human colon epithelial cells (https://www.proteinatlas.org/ENSG00000101842-VSIG1/tissue/colon#img), (2) that it is generally not expressed (with only rare structural alterations) in a majority of advanced tumors (Chen et al., 2012Chen Y. Pan K. Li S. Xia J. Wang W. Chen J. Zhao J. Lü L. Wang D. Pan Q. et al.Decreased expression of V-set and immunoglobulin domain containing 1 (VSIG1) is associated with poor prognosis in primary gastric cancer.J. Surg. Oncol. 2012; 106: 286-293Crossref PubMed Scopus (15) Google Scholar; Inoue et al., 2017Inoue Y. Matsuura S. Yoshimura K. Iwashita Y. Kahyo T. Kawase A. Tanahashi M. Maeda M. Ogawa H. Inui N. et al.Characterization of V-set and immunoglobulin domain containing 1 exerting a tumor suppressor function in gastric, lung, and esophageal cancer cells.Cancer Sci. 2017;" @default.
• W3099741968 created "2020-11-23" @default.
• W3099741968 creator A5036418270 @default.
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