How cancer arises from a single normal cell is still the subject of active debate, affecting intervention strategies. While many cells may harbor oncogenic mutations, only a few unpredictably end up developing a full-blown tumor (Cairns, 1975; Riva et al., 2020; Adashek et al., 2020; Hanahan and Weinberg, 2011). Various theories have been proposed to explain that transition (Soto and Sonnenschein, 2011; Tomasetti and Vogelstein, 2015; Jassim et al., 2023; Waclaw et al., 2015), but none has been tested in vivo at the single-cell level. Cancer initiation is thus believed to be a rare event taking place at the level of individual cells (Visvader, 2011; Nowell, 1976; Hanahan, 2022; Pénisson et al., 2022; Frumkin et al., 2008) arising as a result of the accumulation of genetic mutations in so-called Mut-driver genes (oncogenes such as KRAS [Malumbres and Barbacid, 2003; Pylayeva-Gupta et al., 2011], tumor suppressor genes [Strachan and Read, 1999] such as TP53 or lifespan [Blasco, 2005] genes such as TERT). Notably, mutant KRAS is the most frequent driver of several cancers (COSMIC, 2024; Punekar et al., 2022): about 27% of all human cancers, 45% of colorectal, and 90% of pancreatic cancers (Merz et al., 2021).

Recently, it has been shown that genes involved in embryonic development, pluri/multipotency, and cell reprogramming such as VENTX/NANOG and POU5/OCT4 are abnormally reactivated in late cancer stages, where acting as Epigenetic Drivers (Epi-Drivers) they empower cancer cells with cancer stem cell (CSCs) features, resistance to anticancer therapies, and potential for cancer recurrence/relapse (Vogelstein et al., 2013; Hepburn et al., 2019; Villodre et al., 2016; Ducos et al., 2022; Rawat et al., 2010; Laise, 2022; Park et al., 2021). Although it is evident that such Epi-Drivers confer a selective advantage to CSCs in a full-blown cancer, whether they play a role during the early phases of malignant transformation is still unknown.

Current approaches to the study of cancer use constitutive or conditional expression of Mut-driver (or Epi-driver genes; Shibata et al., 2018) in specific tissues, i.e., in many cells, even though only a small subset of these cells eventually leads to the growth of tumors (Adashek et al., 2020), often observed when the tumor already consists of many thousands of heterogeneous abnormal cells. Hence, carcinogenetic processes observed among sibling organisms (Baggiolini et al., 2021; Ablain et al., 2018; Kaufman et al., 2016) occur with variable latency period from the onset of induction, at different locations and develop asynchronously. As a result, the initial stages of tumorigenesis are difficult to study, the state of the cell(s) of origin difficult to assess and control while its cellular environment is perturbed by the induced expression of the Mut- or Epi-driver genes. Due to the rarity of the event in vivo (Kaufman et al., 2016), a statistically relevant single-cell tracking and characterization of the early stages of tumorigenesis has therefore never been done. This emphasizes the need to predict or control the cell undergoing malignant transformation in vivo in order to pave the way for a study of the cellular and molecular events involved in the initial stages of tumorigenesis.

To address these issues, we have developed an optogenetic approach to control the expression of an oncogene in a single cell (Feng et al., 2017), with the goals of: (1) measuring the probability of the malignant transformation of a single cell in a live organism in various backgrounds and (2) tracking and characterizing the development of a tumor from the original cell. In the following we show the synergy between only two factors: the oncogene kRasG12V and a reprogramming factor (Ventx, Nanog, or Oct4) increases the probability of carcinogenesis from a single cell by many orders of magnitude when compared to the expression of either of these genes (or none).

Optogenetics approaches allow for the photocontrol and monitoring of the activity of biomolecules in vivo. The approach we developed uses a photoactivable analog of tamoxifen (caged cyclofen [cCYC]) to control the activity of proteins fused to the ERT-receptor (Sinha et al., 2010; Zhang et al., 2018) (a modified estrogen binding domain; Feil et al., 1996; Figure 1A). These protein constructs are sequestered by cytoplasmic chaperones. Once cCYC is uncaged by light (with one-photon illumination at ~375 nm or two-photon at ~750 nm), cyclofen (CYC) is released (Sinha et al., 2010). It binds to the ERT-receptor and releases the fused protein (e.g. a Cre-ERT recombinase) from its complex with cytoplasmic chaperones (Zhang et al., 2018; Figure 1A).

Figure 1 with 6 supplements see all

Download asset Open asset

We used this approach to photocontrol the activity of a Cre/loxP recombination system in a transgenic zebrafish line (Tgactin:loxP-EOS-stop-loxP-KRASG12V-T2A-H2B-mTFP; ubi:Cre-ERT; myl7:EGFP) carrying a floxable (loxP flanked) EOS gene (coding for a green fluorescent protein) upstream from the oncogene (Feng et al., 2017; Sinha et al., 2010) (KRASG12V). In such a transgenic zebrafish line hereafter referred to as KRASG12V line (Figure 1B, Figure 1—figure supplement 1B), the expression of EOS can be switched to KRASG12V and H2B-mTFP (nuclear blue fluorescence) upon 1 hr incubation in cCYC, followed by washing and UV (365 nm) cCYC uncaging which triggers CRE-ERT activation (Figure 1C); mTFP fluorescence can be observed about 30 min post-illumination (Figure 1—figure supplement 1B) and is stably maintained in zebrafish (Figure 1G, Figure 1—figure supplement 1C). Consistent with an acquired refractory cell state and a loss of oncogenic competence (Baggiolini et al., 2021), whole body expression of the oncogene at 1 day post-fertilization (dpf) did not result in tumorigenesis. Notice that consistent with previous studies (Feng et al., 2017; Sinha et al., 2010; Zhang et al., 2018), where cCYC stability has been validated both in vitro (by chemical analyses) and in vivo (with fluorescent reporters of uncaging), in absence of illumination no spontaneous uncaging of cCYC and thus activation of the oncogene (i.e. blue fluorescent cells) is observed. Notice also that once cCYC is uncaged embryos are maintained in E3 medium with no CYC and thus no activation of the oncogene.

To test for the possible impact of a reprogramming factor on the malignant transition, embryos from this transgenic line were injected at the one-cell stage with the mRNA of a construct consisting of a glucocorticoid receptor (GR) fused with a reprogramming factor (Ventx, Nanog, or Oct4; Ducos et al., 2022; Scerbo and Monsoro-Burq, 2020; Figure 1—figure supplement 2A). The resulting protein (e.g. Ventx-GR) is sequestered by cytoplasmic chaperones and transiently activated upon incubation of zebrafish in dexamethasone (DEX) (Figure 1—figure supplement 2B). Activation of the oncogene (Feng et al., 2017) at 1 dpf, if and only if followed by transient activation of Ventx (or Nanog or Oct4), did yield reproducible hyperplasic outgrowths in many different tissues (including brain, intestine, pancreas, and liver; Figure 1—figure supplement 3A–C). Immunohistochemistry detection of phospho-ERK activity (Figure 1—figure supplement 4A and B), hematoxylin and eosin (H&E) staining (Figure 1—figure supplement 4C), decreased survival (Figure 1—figure supplement 4D), and reverse transcriptase quantitative PCR (RT-qPCR) of selected genes (Figure 1—figure supplement 5; Figure 1—figure supplement 6A and B) all display features associated with tumorigenesis. None of the controls (activation of kRasG12V only, Ventx-GR only, incubation in CYC or DEX, etc.) developed hyperplasia, but rather grew into normal zebrafish (Figure 1—figure supplement 1B and C; Figure 1—figure supplement 3A). These controls imply that the observed tumor is only a result of the joint activation of kRasG12V (permanently) and a reprogramming factor (transiently).

Since both kRasG12V and Ventx have been reported to play a role in brain cancer (Lam et al., 2022), we decided to activate the oncogene kRasG12V at 1 dpf in a single normal cell of the brain of a transgenic zebrafish (injected with the mRNA of Ventx-GR at one-cell stage). Activation of the oncogene in a single cell was achieved by 1 hr incubation in cCYC, followed by washing and illumination at 405 nm to uncage cCYC in a small area (diameter ~80 μm) of the brain (in the vicinity of the otic vesicle) (Figure 1G). Notice that following uncaging the embryos are maintained in a CYC-free E3 medium.

Subsequent to cCYC uncaging, with probability ~50% the oncogene is activated in a single cell of the illuminated area (activation statistics is shown in Figure 2A), identified within ~1 hr by the blue fluorescence of its nuclear marker (H2B-mTFP), see Figures 1G and 2A, Figure 2—figure supplement 1. H2B-mTFP positive cells are only observed at the site of illumination (i.e. brain), attesting to the precision of our optogenetic method (Figure 2A). This observation specifies whether one, two, or more cells were activated (Figure 2—figure supplement 1) within ~1 hr post-illumination. Notice that activation of the oncogene alone does not give rise to cancer (the activated cell usually disappears within a few days, Figure 2—figure supplement 2A), which implies that the proliferation of kRasG12V positive (mTFP⁺, blue fluorescent) cells - reported next - is not due to late activation of the oncogene (and its fluorescent marker).

Figure 2 with 4 supplements see all

Download asset Open asset

Indeed, if and only if following the local expression of the oncogene, Ventx-GR is transiently activated (by 24 hr incubation in DEX) does the cell divide and proliferate (Figure 2B and C, Figure 2—figure supplement 3, Figure 2—figure supplement 4). We observed that at 1 day post-induction (dpi) ~50% of the induced cells had divided and expanded clonally by a factor ~3 (Figure 2B). In the other 50% of cases, the activated single cell had neither divided nor died (Figure 2B). Surprisingly, at 3–5 dpi, we observed in all zebrafish larvae (n=30) that the induced cell(s) gave rise to progeny that display short-range dispersion (Figure 2—figure supplement 4A and B) and then give rise to a tumor mass in the brain with local infiltration of malignant cells (Figure 2C). H&E staining of the brain tissue (Figure 2D and E) and metastases (Figure 3) further confirm the malignant state of the induced cell and its progeny.

Figure 3

Download asset Open asset

In parallel with tumor mass formation in the brain (Figure 2), we observed that some cells of the progeny re-localized to new loci far from the brain, such as the heart (Figure 3A), the digestive tract (Figure 3B; Figure 2—figure supplement 4D), and the trunk (Figure 2—figure supplement 3B; Figure 2—figure supplement 4C). We therefore deduce that the transient activation of Ventx alters the state of a cell expressing a mutated oncogene (i.e. KRASG12V) and induces its malignant transformation in vivo, with tumorigenic potential and the capacity to generate invasive progeny. Notice that metastatic cells must be the progeny of the initial (blue fluorescent) cell as the larvae are incubated in embryo medium with no cCYC. Furthermore, absent VentX activation we do not observe proliferation and metastatic behavior, rather the initially induced cell disappears, see Figure 2—figure supplement 2A.

Of the larvae in which a few (1–6) cells were expressing the oncogene and in which Ventx was transiently activated (KR+VX cell), all (N=30) developed tumors within 5 dpi. The frequency of tumor development is therefore F₁=1. Conversely, the probability for such a cell to not develop a tumor is F₀=0. Due to the finite size of the sample, we estimate the probability of tumor development from a single cell to be: P₁>80% (χ²=3.75; df = 1).

To definitely confirm the carcinogenic nature of the KR+VX-induced cells, we isolated and injected a single cell from a hyperplasic tissue (identified by the blue fluorescence of its nucleus) into a naïve host zebrafish larva. This led to integration, migration, and colonization of the host tissues by the progeny of the transplanted cell (Figure 4A and B), with strong pERK activity detected in tumor masses of the host (Figure 4B). Out of 52 transplanted host zebrafish, 31 developed tumors, a probability of tumor development (60%) consistent with previously reported efficiency of tumor cells transplantation in zebrafish (Campbell et al., 2021). The capacity of the progeny of the induced cells to metastasize and develop a tumor mass when transplanted in a naïve zebrafish is - to the best of our knowledge - the operational definition of a malignant tumor. This result definitely confirms the cancerous nature of cells expressing a mutated oncogene and exposed to the transient activation of Ventx, with subsequent alteration of the homeostasis. Notice that injection into a naïve larva of an oncogene expressing cell (identified by its nuclear blue fluorescence) which did not experience the transient activation of a reprogramming factor does not yield a tumor and disappears within a few days (Figure 2—figure supplement 2B).

Figure 4

Download asset Open asset

The surprising frequency of somatic mutations occurring in physiologically normal tissues (Martincorena and Campbell, 2015; Brash, 2015) raises the question of what combinations of events are sufficient for the malignant transformation of a single cell and the rise of the cell of origin of cancer. The robust deterministic process of single-cell cancer induction that we uncovered suggests that the aberrant reactivation of Epi-Driver genes involved in reprogramming/pluripotency (such as VENTX/NANOG, POU5/OCT4) might be relevant to the irreversible malignant transformation of a cell. Reprogramming Epi-Drivers are important regulators of cell viability, survival, and proliferation in several cellular contexts, from embryonic stem cells to cancer cells (Hepburn et al., 2019; Villodre et al., 2016; Ducos et al., 2022; Rawat et al., 2010; Laise, 2022; Park et al., 2021). Due to their capacity to modulate epigenetic memory and cell plasticity, these reprogramming factors may drive the early stages of malignant transformation in vivo once (re)activated aberrantly. They likely share some mechanism(s) with processes such as induced nuclear reprogramming (Hepburn et al., 2019; Villodre et al., 2016; Ducos et al., 2022), pluripotency maintenance, and/or endogenous cell reprogramming during development (Scerbo and Monsoro-Burq, 2020). Thus, consistent with our results, the reactivation (Scerbo and Monsoro-Burq, 2020) of the Neural Crest (NC) Progenitor program (possibly via the stochastic expression of VENTX/NANOG and/or POU5/OCT4) has been shown in BRAF/p53 double mutant cells to yield NC-related tumors in vivo (Baggiolini et al., 2021; Ablain et al., 2018; Kaufman et al., 2016). We surmise that activation of Epi-Driver genes in NC-related tumors might confer predictable tumorigenesis.

Since cells carrying cancer-causing mutations do not deterministically developed cancers in vivo (Kaufman et al., 2016; Campbell et al., 2021), we suggest that the probability of a mutated cell to enter into malignant transformation, or to maintain its physiological functions despite mutations, correlates with the probability of the reactivation of reprogramming factors. Our results support a ‘Vogelgram’ (Fearon and Vogelstein, 1990) two-step model (Figure 5) for irreversible single-cell malignant transformation in vivo, mirroring the two hits hypothesis proposed by Berenblum and Shubik, 1949.

Figure 5

Download asset Open asset

Thus, the appearance of Mut-Drivers in a normal (preprocancer; Brash, 2015) cell (Martincorena and Campbell, 2015) within healthy tissue would act as a permissive but insufficient initiator for malignant cell transformation. Despite the frequency of mutational insults, which are often caused by external cues (chemical carcinogens, UV, etc.) or senescence/aging, it is known that surveillance mechanisms like cell competition between wild-type and mutated cells safeguard tissue homeostasis and results in active elimination of mutant cells from the tissue (Brown et al., 2017; Porazinski et al., 2016; van Neerven and Vermeulen, 2023). Our data imply that the aberrant reactivation of Epi-Driver genes involved in reprogramming/pluripotency may promote the irreversible and deterministic malignant transformation in the cell of origin of cancer leading to carcinogenesis.

Our hypothesis is further strengthened by the observation that the variation in cancer risk among tissues can be related to the number of stem cell divisions and tissue renewal (Tomasetti and Vogelstein, 2015). The aberrant reactivation (or maintenance) of Epi-Driver genes in these regenerative events, in cooperation with incident mutations, might be the key deterministic factor in the switching of a normal/healthy cell into the first cell of origin of cancer.

Furthermore, the results reported here are compatible with the recently proposed ‘ground state theory of cancer initiation’ (Jassim et al., 2023). According to that theory a malignant transformation may occur in a cell harboring an oncogenic mutation upon a change of its functional state (its ‘ground state’). Here, the transient activation of a reprogramming factor (i.e. Ventx, Nanog, or Oct4) possibly synergizes with the oncogene to alter the epigenetic and functional state of the cell allowing its transformation into a tumorigenic cell.

This may also explain the apparent discrepancy between the present deterministic and rapid tumor development upon activation in a single cell of both kRasG12V and a reprogramming factor and the reports of stochastic and random tumor development over weeks following activation of kRasG12V in a whole tissue (brain [Ju et al., 2015], pancreas [Oh and Park, 2019], or intestine [Lu et al., 2018]). In the latter case, transformed cells may have been triggered to undergo a malignant transformation by the stochastic activation of a reprogramming factor/Epi-Driver, which is consistent with the recent finding that cancer initiation might occur by transient perturbations of epigenetic regulators (Parreno et al., 2024).

Importantly, our observation that the progeny of the cell(s) of origin of cancer display a short-range dispersal within the tissue during the earliest phases of tumorigenesis and prior to the effective appearance of tumor mass corroborates a recent theoretical model predicting such an early dispersal during carcinogenesis to explain heterogeneity, therapeutic resistance, and tumor relapse (Waclaw et al., 2015).

Being the first experiments to predictably and noninvasively control the malignant transformation of a single cell in an unaltered microenvironment, our approach opens a new vista on the study of ‘the cell of origin of cancer’ (Blanpain, 2013), an acknowledged enigma of cancer research. As such our results raise many questions: what mechanisms are at work in the transition from the preprocancer state to a tumorigenic state? Is the aberrant reactivation of reprogramming factors a key step in the initiation of carcinogenesis, independent of age, tissue, or oncogenic mutation? Do drugs against reprogramming factors reduce the probability of carcinogenesis? Conversely, do some carcinogens (e.g. environmental pollutants, pesticides, endocrine disruptors, etc.) act by reactivating those factors?

By allowing for specific and reproducible single-cell malignant transformation in vivo, our optogenetic approach opens the way for a quantitative study of the initial stages of cancer at the single-cell level (e.g. tracking and characterization), that will allow one to address many of these questions.

RTqPCR data files for Figure 1—figure supplement 5 and 6 have been provided in Figure 1—figure supplement 5—source data 1.

1. 1. Ablain J
   2. Xu M
   3. Rothschild H
   4. Jordan RC
   5. Mito JK
   6. Daniels BH
   7. Bell CF
   8. Joseph NM
   9. Wu H
   10. Bastian BC
   11. Zon LI
   12. Yeh I

   (2018) Human tumor genomics and zebrafish modeling identify SPRED1 loss as a driver of mucosal melanoma

   Science 362:1055–1060.

   https://doi.org/10.1126/science.aau6509

   • PubMed
   • Google Scholar
2. 1. Adashek JJ
   2. Kato S
   3. Lippman SM
   4. Kurzrock R

   (2020) The paradox of cancer genes in non-malignant conditions: implications for precision medicine

   Genome Medicine 12:16.

   https://doi.org/10.1186/s13073-020-0714-y

   • PubMed
   • Google Scholar
3. 1. Baggiolini A
   2. Callahan SJ
   3. Montal E
   4. Weiss JM
   5. Trieu T
   6. Tagore MM
   7. Tischfield SE
   8. Walsh RM
   9. Suresh S
   10. Fan Y
   11. Campbell NR
   12. Perlee SC
   13. Saurat N
   14. Hunter MV
   15. Simon-Vermot T
   16. Huang T-H
   17. Ma Y
   18. Hollmann T
   19. Tickoo SK
   20. Taylor BS
   21. Khurana E
   22. Koche RP
   23. Studer L
   24. White RM

   (2021) Developmental chromatin programs determine oncogenic competence in melanoma

   Science 373:eabc1048.

   https://doi.org/10.1126/science.abc1048

   • PubMed
   • Google Scholar
4. 1. Berenblum I
   2. Shubik P

   (1949) The Persistence of latent tumour cells induced in the mouse’s skin by a single application of 9:10-Dimethyl-1:2-benzanthracene

   British Journal of Cancer 3:384–386.

   https://doi.org/10.1038/bjc.1949.42

   • Google Scholar
5. 1. Blanpain C

   (2013) Tracing the cellular origin of cancer

   Nature Cell Biology 15:126–134.

   https://doi.org/10.1038/ncb2657

   • Google Scholar
6. 1. Blasco MA

   (2005) Telomeres and human disease: ageing, cancer and beyond

   Nature Reviews. Genetics 6:611–622.

   https://doi.org/10.1038/nrg1656

   • PubMed
   • Google Scholar
7. 1. Brash DE

   (2015) Preprocancer: normal skin harbor cancer causing mutations

   Science 1:867–868.

   https://doi.org/10.1126/science.aac4435

   • Google Scholar
8. 1. Bresciani E
   2. Broadbridge E
   3. Liu PP

   (2018) An efficient dissociation protocol for generation of single cell suspension from zebrafish embryos and larvae

   MethodsX 5:1287–1290.

   https://doi.org/10.1016/j.mex.2018.10.009

   • PubMed
   • Google Scholar
9. 1. Brown S
   2. Pineda CM
   3. Xin T
   4. Boucher J
   5. Suozzi KC
   6. Park S
   7. Matte-Martone C
   8. Gonzalez DG
   9. Rytlewski J
   10. Beronja S
   11. Greco V

   (2017) Correction of aberrant growth preserves tissue homeostasis

   Nature 548:334–337.

   https://doi.org/10.1038/nature23304

   • PubMed
   • Google Scholar
10. 1. Cairns J

    (1975) Mutation selection and the natural history of cancer

    Nature 255:197–200.

    https://doi.org/10.1038/255197a0

    • PubMed
    • Google Scholar
11. 1. Campbell NR
    2. Rao A
    3. Hunter MV
    4. Sznurkowska MK
    5. Briker L
    6. Zhang M
    7. Baron M
    8. Heilmann S
    9. Deforet M
    10. Kenny C
    11. Ferretti LP
    12. Huang T-H
    13. Perlee S
    14. Garg M
    15. Nsengimana J
    16. Saini M
    17. Montal E
    18. Tagore M
    19. Newton-Bishop J
    20. Middleton MR
    21. Corrie P
    22. Adams DJ
    23. Rabbie R
    24. Aceto N
    25. Levesque MP
    26. Cornell RA
    27. Yanai I
    28. Xavier JB
    29. White RM

    (2021) Cooperation between melanoma cell states promotes metastasis through heterotypic cluster formation

    Developmental Cell 56:2808–2825.

    https://doi.org/10.1016/j.devcel.2021.08.018

    • PubMed
    • Google Scholar
12. Website

    1. COSMIC

    (2024) Catalogue of Somatic Mutations in Cancer

    Accessed November 19, 2024.

    https://cancer.sanger.ac.uk/cosmic
13. 1. Ducos B
    2. Bensimon D
    3. Scerbo P

    (2022) Vertebrate cell differentiation, evolution, and diseases: the vertebrate-specific developmental potential guardians VENTX/NANOG and POU5/OCT4 enter the stage

    Cells 11:2299.

    https://doi.org/10.3390/cells11152299

    • Google Scholar
14. 1. Fearon ER
    2. Vogelstein B

    (1990) A genetic model for colorectal tumorigenesis

    Cell 61:759–767.

    https://doi.org/10.1016/0092-8674(90)90186-i

    • PubMed
    • Google Scholar
15. 1. Feil R
    2. Brocard J
    3. Mascrez B
    4. LeMeur M
    5. Metzger D
    6. Chambon P

    (1996) Ligand-activated site-specific recombination in mice

    PNAS 93:10887–10890.

    https://doi.org/10.1073/pnas.93.20.10887

    • PubMed
    • Google Scholar
16. 1. Feng Z
    2. Nam S
    3. Hamouri F
    4. Aujard I
    5. Ducos B
    6. Vriz S
    7. Volovitch M
    8. Jullien L
    9. Lin S
    10. Weiss S
    11. Bensimon D

    (2017) Optical control of tumor induction in the Zebrafish

    Scientific Reports 7:9195.

    https://doi.org/10.1038/s41598-017-09697-x

    • PubMed
    • Google Scholar
17. 1. Frumkin D
    2. Wasserstrom A
    3. Itzkovitz S
    4. Stern T
    5. Harmelin A
    6. Eilam R
    7. Rechavi G
    8. Shapiro E

    (2008) Cell lineage analysis of a mouse tumor

    Cancer Research 68:5924–5931.

    https://doi.org/10.1158/0008-5472.CAN-07-6216

    • PubMed
    • Google Scholar
18. 1. Hanahan D
    2. Weinberg RA

    (2011) Hallmarks of cancer: the next generation

    Cell 144:646–674.

    https://doi.org/10.1016/j.cell.2011.02.013

    • PubMed
    • Google Scholar
19. 1. Hanahan D

    (2022) Hallmarks of cancer: new dimensions

    Cancer Discovery 12:31–46.

    https://doi.org/10.1158/2159-8290.CD-21-1059

    • PubMed
    • Google Scholar
20. 1. Hepburn AC
    2. Steele RE
    3. Veeratterapillay R
    4. Wilson L
    5. Kounatidou EE
    6. Barnard A
    7. Berry P
    8. Cassidy JR
    9. Moad M
    10. El-Sherif A
    11. Gaughan L
    12. Mills IG
    13. Robson CN
    14. Heer R

    (2019) The induction of core pluripotency master regulators in cancers defines poor clinical outcomes and treatment resistance

    Oncogene 38:4412–4424.

    https://doi.org/10.1038/s41388-019-0712-y

    • PubMed
    • Google Scholar
21. 1. Jassim A
    2. Rahrmann EP
    3. Simons BD
    4. Gilbertson RJ

    (2023) Cancers make their own luck: theories of cancer origins

    Nature Reviews. Cancer 23:710–724.

    https://doi.org/10.1038/s41568-023-00602-5

    • PubMed
    • Google Scholar
22. 1. Ju B
    2. Chen W
    3. Orr BA
    4. Spitsbergen JM
    5. Jia S
    6. Eden CJ
    7. Henson HE
    8. Taylor MR

    (2015) Oncogenic KRAS promotes malignant brain tumors in zebrafish

    Molecular Cancer 14:18.

    https://doi.org/10.1186/s12943-015-0288-2

    • PubMed
    • Google Scholar
23. 1. Kaufman CK
    2. Mosimann C
    3. Fan ZP
    4. Yang S
    5. Thomas AJ
    6. Ablain J
    7. Tan JL
    8. Fogley RD
    9. van Rooijen E
    10. Hagedorn EJ
    11. Ciarlo C
    12. White RM
    13. Matos DA
    14. Puller A-C
    15. Santoriello C
    16. Liao EC
    17. Young RA
    18. Zon LI

    (2016) A zebrafish melanoma model reveals emergence of neural crest identity during melanoma initiation

    Science 351:aad2197.

    https://doi.org/10.1126/science.aad2197

    • PubMed
    • Google Scholar
24. 1. Kimmel CB
    2. Ballard WW
    3. Kimmel SR
    4. Ullmann B
    5. Schilling TF

    (1995) Stages of embryonic development of the zebrafish

    Developmental Dynamics 203:253–310.

    https://doi.org/10.1002/aja.1002030302

    • PubMed
    • Google Scholar
25. Preprint

    1. Laise P

    (2022) Developmental and MAPK-responsive transcription factors drive distinct malignant subtypes and genetic dependencies in pancreatic cancer

    bioRxiv.

    https://doi.org/10.1101/2020.10.27.357269

    • Google Scholar
26. 1. Lam KHB
    2. Leon AJ
    3. Hui W
    4. Lee SC-E
    5. Batruch I
    6. Faust K
    7. Klekner A
    8. Hutóczki G
    9. Koritzinsky M
    10. Richer M
    11. Djuric U
    12. Diamandis P

    (2022) Topographic mapping of the glioblastoma proteome reveals a triple-axis model of intra-tumoral heterogeneity

    Nature Communications 13:116.

    https://doi.org/10.1038/s41467-021-27667-w

    • PubMed
    • Google Scholar
27. 1. Livak KJ
    2. Schmittgen TD

    (2001) Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method

    Methods 25:402–408.

    https://doi.org/10.1006/meth.2001.1262

    • PubMed
    • Google Scholar
28. 1. Lu J-W
    2. Raghuram D
    3. Fong P-SA
    4. Gong Z

    (2018) Inducible intestine-specific expression of kras^V12 triggers intestinal tumorigenesis in transgenic zebrafish

    Neoplasia 20:1187–1197.

    https://doi.org/10.1016/j.neo.2018.10.002

    • PubMed
    • Google Scholar
29. 1. Malumbres M
    2. Barbacid M

    (2003) RAS oncogenes: the first 30 years

    Nature Reviews. Cancer 3:459–465.

    https://doi.org/10.1038/nrc1097

    • PubMed
    • Google Scholar
30. 1. Martincorena I
    2. Campbell PJ

    (2015) Somatic mutation in cancer and normal cells

    Science 349:1483–1489.

    https://doi.org/10.1126/science.aab4082

    • PubMed
    • Google Scholar
31. 1. Merz V
    2. Gaule M
    3. Zecchetto C
    4. Cavaliere A
    5. Casalino S
    6. Pesoni C
    7. Contarelli S
    8. Sabbadini F
    9. Bertolini M
    10. Mangiameli D
    11. Milella M
    12. Fedele V
    13. Melisi D

    (2021) Targeting KRAS: the elephant in the room of epithelial cancers

    Frontiers in Oncology 11:638360.

    https://doi.org/10.3389/fonc.2021.638360

    • PubMed
    • Google Scholar
32. 1. Mosimann C
    2. Kaufman CK
    3. Li P
    4. Pugach EK
    5. Tamplin OJ
    6. Zon LI

    (2011) Ubiquitous transgene expression and Cre-based recombination driven by the ubiquitin promoter in zebrafish

    Development 138:169–177.

    https://doi.org/10.1242/dev.059345

    • PubMed
    • Google Scholar
33. 1. Nicoli S
    2. Presta M

    (2007) The zebrafish/tumor xenograft angiogenesis assay

    Nature Protocols 2:2918–2923.

    https://doi.org/10.1038/nprot.2007.412

    • PubMed
    • Google Scholar
34. 1. Nowell PC

    (1976) The clonal evolution of tumor cell populations

    Science 194:23–28.

    https://doi.org/10.1126/science.959840

    • PubMed
    • Google Scholar
35. 1. Oh S
    2. Park JT

    (2019) Zebrafish model of KRAS-initiated pancreatic endocrine tumor

    Animal Cells and Systems 23:209–218.

    https://doi.org/10.1080/19768354.2019.1610058

    • PubMed
    • Google Scholar
36. 1. Park Y
    2. Park J
    3. Ahn JW
    4. Sim JM
    5. Kang SJ
    6. Kim S
    7. Hwang SJ
    8. Han S-H
    9. Sung KS
    10. Lim J

    (2021) Transcriptomic landscape of lower grade glioma based on age-related non-silent somatic mutations

    Current Oncology 28:2281–2295.

    https://doi.org/10.3390/curroncol28030210

    • PubMed
    • Google Scholar
37. 1. Parreno V
    2. Loubiere V
    3. Schuettengruber B
    4. Fritsch L
    5. Rawal CC
    6. Erokhin M
    7. Győrffy B
    8. Normanno D
    9. Di Stefano M
    10. Moreaux J
    11. Butova NL
    12. Chiolo I
    13. Chetverina D
    14. Martinez A-M
    15. Cavalli G

    (2024) Transient loss of Polycomb components induces an epigenetic cancer fate

    Nature 629:688–696.

    https://doi.org/10.1038/s41586-024-07328-w

    • PubMed
    • Google Scholar
38. 1. Pénisson S
    2. Lambert A
    3. Tomasetti C

    (2022) Evaluating cancer etiology and risk with a mathematical model of tumor evolution

    Nature Communications 13:7224.

    https://doi.org/10.1038/s41467-022-34760-1

    • PubMed
    • Google Scholar
39. 1. Porazinski S
    2. de Navascués J
    3. Yako Y
    4. Hill W
    5. Jones MR
    6. Maddison R
    7. Fujita Y
    8. Hogan C

    (2016) EphA2 drives the segregation of ras-transformed epithelial cells from normal neighbors

    Current Biology 26:3220–3229.

    https://doi.org/10.1016/j.cub.2016.09.037

    • PubMed
    • Google Scholar
40. 1. Punekar SR
    2. Velcheti V
    3. Neel BG
    4. Wong K-K

    (2022) The current state of the art and future trends in RAS-targeted cancer therapies

    Nature Reviews Clinical Oncology 19:637–655.

    https://doi.org/10.1038/s41571-022-00671-9

    • Google Scholar
41. 1. Pylayeva-Gupta Y
    2. Grabocka E
    3. Bar-Sagi D

    (2011) RAS oncogenes: weaving a tumorigenic web

    Nature Reviews. Cancer 11:761–774.

    https://doi.org/10.1038/nrc3106

    • PubMed
    • Google Scholar
42. 1. Rawat VPS
    2. Arseni N
    3. Ahmed F
    4. Mulaw MA
    5. Thoene S
    6. Heilmeier B
    7. Sadlon T
    8. D’Andrea RJ
    9. Hiddemann W
    10. Bohlander SK
    11. Buske C
    12. Feuring-Buske M

    (2010) The vent-like homeobox gene VENTX promotes human myeloid differentiation and is highly expressed in acute myeloid leukemia

    PNAS 107:16946–16951.

    https://doi.org/10.1073/pnas.1001878107

    • PubMed
    • Google Scholar
43. 1. Riva L
    2. Pandiri AR
    3. Li YR
    4. Droop A
    5. Hewinson J
    6. Quail MA
    7. Iyer V
    8. Shepherd R
    9. Herbert RA
    10. Campbell PJ
    11. Sills RC
    12. Alexandrov LB
    13. Balmain A
    14. Adams DJ

    (2020) The mutational signature profile of known and suspected human carcinogens in mice

    Nature Genetics 52:1189–1197.

    https://doi.org/10.1038/s41588-020-0692-4

    • PubMed
    • Google Scholar
44. 1. Scerbo P
    2. Monsoro-Burq AH

    (2020) The vertebrate-specific VENTX/NANOG gene empowers neural crest with ectomesenchyme potential

    Science Advances 6:eaaz1469.

    https://doi.org/10.1126/sciadv.aaz1469

    • PubMed
    • Google Scholar
45. 1. Shibata H
    2. Komura S
    3. Yamada Y
    4. Sankoda N
    5. Tanaka A
    6. Ukai T
    7. Kabata M
    8. Sakurai S
    9. Kuze B
    10. Woltjen K
    11. Haga H
    12. Ito Y
    13. Kawaguchi Y
    14. Yamamoto T
    15. Yamada Y

    (2018) In vivo reprogramming drives Kras-induced cancer development

    Nature Communications 9:2081.

    https://doi.org/10.1038/s41467-018-04449-5

    • PubMed
    • Google Scholar
46. 1. Sinha DK
    2. Neveu P
    3. Gagey N
    4. Aujard I
    5. Le Saux T
    6. Rampon C
    7. Gauron C
    8. Kawakami K
    9. Leucht C
    10. Bally-Cuif L
    11. Volovitch M
    12. Bensimon D
    13. Jullien L
    14. Vriz S

    (2010) Photoactivation of the CreER T2 recombinase for conditional site-specific recombination with high spatiotemporal resolution

    Zebrafish 7:199–204.

    https://doi.org/10.1089/zeb.2009.0632

    • PubMed
    • Google Scholar
47. 1. Soto AM
    2. Sonnenschein C

    (2011) The tissue organization field theory of cancer: a testable replacement for the somatic mutation theory

    BioEssays 33:332–340.

    https://doi.org/10.1002/bies.201100025

    • PubMed
    • Google Scholar
48. Book

    1. Strachan T
    2. Read AP

    (1999)

    Human Molecular Genetics.Chapter 18

    New York: Wiley-Liss.

    • Google Scholar
49. 1. Tomasetti C
    2. Vogelstein B

    (2015) Cancer etiology.:Variation in cancer risk among tissues can be explained by the number of stem cell divisions

    Science 347:78–81.

    https://doi.org/10.1126/science.1260825

    • PubMed
    • Google Scholar
50. 1. van Neerven SM
    2. Vermeulen L

    (2023) Cell competition in development, homeostasis and cancer

    Nature Reviews. Molecular Cell Biology 24:221–236.

    https://doi.org/10.1038/s41580-022-00538-y

    • PubMed
    • Google Scholar
51. 1. Villodre ES
    2. Kipper FC
    3. Pereira MB
    4. Lenz G

    (2016) Roles of OCT4 in tumorigenesis, cancer therapy resistance and prognosis

    Cancer Treatment Reviews 51:1–9.

    https://doi.org/10.1016/j.ctrv.2016.10.003

    • PubMed
    • Google Scholar
52. 1. Visvader JE

    (2011) Cells of origin in cancer

    Nature 469:314–322.

    https://doi.org/10.1038/nature09781

    • PubMed
    • Google Scholar
53. 1. Vogelstein B
    2. Papadopoulos N
    3. Velculescu VE
    4. Zhou S
    5. Diaz LA Jr
    6. Kinzler KW

    (2013) Cancer genome landscapes

    Science 339:1546–1558.

    https://doi.org/10.1126/science.1235122

    • PubMed
    • Google Scholar
54. 1. Waclaw B
    2. Bozic I
    3. Pittman ME
    4. Hruban RH
    5. Vogelstein B
    6. Nowak MA

    (2015) A spatial model predicts that dispersal and cell turnover limit intratumour heterogeneity

    Nature 525:261–264.

    https://doi.org/10.1038/nature14971

    • PubMed
    • Google Scholar
55. Book

    1. Westerfield M

    (2000)

    The Zebrafish Book. A Guide for the Laboratory Use of Zebrafish (Danio rerio)

    University of Oregon Press.

    • Google Scholar
56. 1. Zhang W
    2. Hamouri F
    3. Feng Z
    4. Aujard I
    5. Ducos B
    6. Ye S
    7. Weiss S
    8. Volovitch M
    9. Vriz S
    10. Jullien L
    11. Bensimon D

    (2018) Control of protein activity and gene expression by cyclofen-OH uncaging

    Chembiochem 19:1232–1238.

    https://doi.org/10.1002/cbic.201700630

    • PubMed
    • Google Scholar
57. 1. Zhang W
    2. Scerbo P
    3. Delagrange M
    4. Candat V
    5. Mayr V
    6. Vriz S
    7. Distel M
    8. Ducos B
    9. Bensimon D

    (2022) Fgf8 dynamics and critical slowing down may account for the temperature independence of somitogenesis

    Communications Biology 5:113.

    https://doi.org/10.1038/s42003-022-03053-0

    • PubMed
    • Google Scholar

Author details

1. Pierluigi Scerbo

   1. Laboratoire de Physique de l’Ecole Normale Supérieure LPENS, ENS, PSL Research University, CNRS, Sorbonne Université, Université de Paris, Paris, France
   2. Inovarion, Paris, France

   Contribution

   Conceptualization, Investigation, Methodology, Writing – original draft, Writing – review and editing

   For correspondence

   pierluigi.scerbo@phys.ens.fr

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3360-4367

2. Benjamin Tisserand

   Laboratoire de Physique de l’Ecole Normale Supérieure LPENS, ENS, PSL Research University, CNRS, Sorbonne Université, Université de Paris, Paris, France

   Contribution

   Methodology

   Competing interests

   No competing interests declared

3. Marine Delagrange

   1. Laboratoire de Physique de l’Ecole Normale Supérieure LPENS, ENS, PSL Research University, CNRS, Sorbonne Université, Université de Paris, Paris, France
   2. High Throughput qPCR Core Facility of the ENS, Ecole Normale Supérieure, PSL Research University, IBENS, Paris, France

   Contribution

   Data curation, Software, Formal analysis, Methodology

   Competing interests

   No competing interests declared

4. Héloise Debare

   Laboratoire de Physique de l’Ecole Normale Supérieure LPENS, ENS, PSL Research University, CNRS, Sorbonne Université, Université de Paris, Paris, France

   Contribution

   Data curation, Software, Methodology

   Competing interests

   No competing interests declared

5. David Bensimon

   1. Laboratoire de Physique de l’Ecole Normale Supérieure LPENS, ENS, PSL Research University, CNRS, Sorbonne Université, Université de Paris, Paris, France
   2. Dept. Chemistry and Biochemistry, UCLA, Los Angeles, United States

   Contribution

   Conceptualization, Formal analysis, Supervision, Methodology, Writing – original draft, Project administration, Writing – review and editing

   For correspondence

   david@lps.ens.fr

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1971-9907

6. Bertrand Ducos

   1. Laboratoire de Physique de l’Ecole Normale Supérieure LPENS, ENS, PSL Research University, CNRS, Sorbonne Université, Université de Paris, Paris, France
   2. High Throughput qPCR Core Facility of the ENS, Ecole Normale Supérieure, PSL Research University, IBENS, Paris, France

   Contribution

   Conceptualization, Resources, Data curation, Methodology, Writing – original draft, Project administration

   For correspondence

   bertrand.ducos@ens.fr

   Competing interests

   No competing interests declared

• 360

  views

• 7

  downloads

• 0

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.