Reducing MYC’s transcriptional footprint unveils a good prognostic gene signature in melanoma Mariano F. Zacarías-Fluck,1 Daniel Massó-Vallés,1,2,3 Fabio Giuntini,1 Íñigo González-Larreategui,1,4 Jastrinjan Kaur,1 Sílvia Casacuberta-Serra,1,2 Toni Jauset,1,2,3 Sandra Martínez-Martín,1,2,3 Génesis Martín-Fernández,1,2 Erika Serrano del Pozo,1 Laia Foradada,2 Judit Grueso,1,2 Lara Nonell,5 Marie-Eve Beaulieu,1,2 Jonathan R. Whitfield,1 and Laura Soucek1,2,3,6 1 Models of Cancer Therapies Group, Vall d’Hebron Institute of Oncology (VHIO), Vall d’Hebron Barcelona Hospital Campus, Barcelona, Spain; 2Peptomyc SL, Vall d’Hebron Barcelona Hospital Campus, 08035 Barcelona, Spain; 3Department of Biochemistry and Molecular Biology, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain; 4Department of Cellular Biology, Phisiology, and Immunology, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallès, Spain; 5Bioinformatics Unit, Vall d’Hebron Institute of Oncology (VHIO), Vall d’Hebron Barcelona Hospital Campus, 08035 Barcelona, Spain; 6Institució Catalana de Recerca i Estudis Avançats (ICREA), 08035 Barcelona, Spain MYC’s key role in oncogenesis and tumor progression has long been established for most human cancers. In mel- anoma, its deregulated activity by amplification of 8q24 chromosome or by upstream signaling coming from acti- vating mutations in the RAS/RAF/MAPK pathway—the most predominantly mutated pathway in this disease— turns MYC into not only a driver but also a facilitator of melanoma progression, with documented effects leading to an aggressive clinical course and resistance to targeted therapy. Here, by making use of Omomyc, the most char- acterized MYC inhibitor to date that has just successfully completed a phase I clinical trial, we show for the first time that MYC inhibition in melanoma induces remarkable transcriptional modulation, resulting in severely compromised tumor growth and a clear abrogation of metastatic capacity independently of the driver mutation. By reducing MYC’s transcriptional footprint in melanoma, Omomyc elicits gene expression profiles remarkably similar to those of patients with good prognosis, underlining the therapeutic potential that such an approach could even- tually have in the clinic in this dismal disease. [Keywords: MYC; Omomyc; melanoma; signature] Supplemental material is available for this article. Received September 5, 2022; revised version accepted March 14, 2023. Cutaneous melanoma, the most aggressive and deadly study by TCGA Network established a genomic classifi- form of skin cancer, is the fifth most diagnosed cancer cation according to driving mutations BRAF, NRAS, in Europe and one of the most frequent causes of cancer NF1, and triple wild type (WT), with potential consequent death. Its incidence and mortality trends are both increas- clinical implications (The Cancer Genome Atlas Net- ing. In 2020, >105,000 people were diagnosed and 16,000 work 2015). Intratumor heterogeneity studies performed have died of this disease in the EU (https://ecis.jrc.ec in the last years have shown that cutaneous melanoma, .europa.eu/factsheets.php, accessed August 8, 2022). In along with lung cancer, harbors a significantly larger mu- the US alone, almost 100,000 new melanoma cases (repre- tational burden than other tumor types. This could poten- senting 5% of all new cancers) are expected to be diag- tially be explained by years of exposure of the skin to UV nosed in 2022, resulting in >7000 deaths (https://seer light, a natural exogenous mutagen, leading to malignant .cancer.gov/statfacts/html/melan.html, accessed August transformation (McGranahan and Swanton 2017). 8, 2022). One main reason for the high death rate associated Melanomas arise from the malignant transformation of with melanoma is its highly aggressive and metastatic na- melanocytes as a result of a combination of exogenous and ture (Shain and Bastian 2016). Current therapeutic options endogenous triggers as well as tumor-intrinsic and im- for stage III and IV metastatic melanoma consist of immu- mune-related factors (Schadendorf et al. 2018). A seminal notherapy (anti-PD-1 alone or in combination with anti- CTLA-4) or, for BRAF mutated melanomas, BRAF Corresponding authors: lsoucek@vhio.net, mzacarias@vhio.net Article published online ahead of print. Article and publication date are © 2023 Zacarías-Fluck et al. This article, published in Genes & Develop- online at http://www.genesdev.org/cgi/doi/10.1101/gad.350078.122. Free- ment, is available under a Creative Commons License (Attribution-Non- ly available online through the Genes & Development Open Access Commercial 4.0 International), as described at http://creativecommons. option. org/licenses/by-nc/4.0/. GENES & DEVELOPMENT 37:303–320 Published by Cold Spring Harbor Laboratory Press; ISSN 0890-9369/23; www.genesdev.org 303 Zacarías-Fluck et al. inhibitors (BRAFis) combined with MEK inhibitors (MEKis) scriptional activity are associated with reduced metastat- (Álamo et al. 2021; Garbe et al. 2022). Although the 5-yr ic capacity and increased immune response, and their overall survival rate has improved thanks to these thera- expression can also be used to identify patients with pies, a significant number of patients do not benefit from good prognosis, highlighting the therapeutic relevance of them due to innate or acquired resistance (Czarnecka MYC inhibition in the context of melanoma. et al. 2020), generating a dire need for new treatments. MYC is a transcription factor acting as a hub that inte- grates intracellular and extracellular cues leading to cell Results growth and proliferation. In order to function, MYC di- Expression of Omomyc-RFP in human melanoma cells merizes with Max to then bind DNA and activate the impairs their proliferation in vitro transcription of genes that allow cells to divide in an or- derly manner. However, its tightly controlled activity is In order to analyze the effect of Omomyc expression in lost in most human cancers, where MYC drives uncon- melanoma cells, we transfected a panel of nine human trolled proliferation (Whitfield and Soucek 2021). melanoma cell lines comprising both BRAF mutated and Melanomas with increased copy number gains in 8q24, wild-type cell lines, with a plasmid coding for the Omo- harboring the MYC locus, occur in nonchronically sun- myc-RFP fusion protein under the control of doxycycline. damaged skin, are amelanotic, and show an aggressive clin- We then evaluated Omomyc’s impact on proliferation by ical course (Pouryazdanparast et al. 2012) with visceral me- Crystal Violet staining in 96-well plates. Despite the fact tastases (Kraehn et al. 2001). MYC overexpression that RFP levels showed high variability of Omomyc ex- (independently of copy number gains) is found in 34% of pression both at the single-cell level and among the differ- melanomas (Bennett 2016), where it has been shown to in- ent cell lines (Supplemental Fig. 1A), at 10 d, Omomyc crease aggressiveness (Schlagbauer-Wadl et al. 1999). In induced a significant decrease in proliferation in all of contrast, MYC down-regulation sensitizes melanomas to them with respect to the untreated control (Fig. 1A). Im- chemotherapy and radiotherapy (Biroccio et al. 2001), sig- portantly, this striking effect was already evident at 3 d nificantly decreasing their proliferation (Mannava et al. in highly proliferating cell lines, (Fig. 1B) and was indepen- 2008). Finally, MYC deregulation is associated with several dent of their mutational profile, as we observed no differ- pathways of treatment resistance (Singleton et al. 2017). ences related to the presence of BRAFV600E, BRAFwt, or Hence, MYC inhibition seems to hold promise for an im- even NRASQ61L/R/K or mutated NF1 (Fig. 1A). Interesting- portant therapeutic impact in all of these contexts. ly, when cells were plated at very low density for colony Our laboratory has developed and validated Omomyc formation studies, this antiproliferative effect was even (Soucek et al. 1998; Massó-Vallés and Soucek 2020), the more noticeable, with very few colonies arising in Omo- best-characterized direct MYC inhibitor to date, which myc-expressing cells compared with their control coun- functions as a dominant negative for MYC transcriptional terparts (Fig. 1C). In a subset of cell lines (namely, A375, activity. In a nutshell, Omomyc sequesters MYC away SkMel37, and SkMel103), Omomyc also triggered cell from DNA and occupies target gene promoters in the death at 7 d (Supplemental Fig. S1B). With the aim of bet- form of inactive homodimers or heterodimers with Max, ter characterizing the effect of Omomyc over a longer pe- shutting down their transcription. The different propor- riod, we analyzed proliferation and cell death up to 4 wk in tions of Omomyc dimers depend on the cellular context, both A375 (Fig. 1D, top panels) and SkMel147 (Fig. 1D, as the relative amounts of MYC and Max will determine bottom panels). In both cell lines, we saw sustained stoichiometrically the likelihood of formation of the dif- growth arrest. In the case of A375, this was accompanied ferent complexes. Omomyc has been used as a transgene by a constant and significant increase in cell death with re- in a wide range of murine cancer models, showing a re- spect to the control from day 7 onward. However, in the markable effect on tumor growth, with only minor, re- case of SkMel147, the increase in cell death was only ev- versible side effects in normal tissues (Massó-Vallés and ident at day 21 and became significant at day 28 (Fig. Soucek 2020). Among these different models, we demon- 1D; Supplemental Fig. S1C). Of note, during this long- strated that transgenic expression of Omomyc in supra- term experiment, RFP levels decreased in both cell lines basal skin of a model of MYC-induced papillomatosis (Supplemental Fig. S1D). This could be explained by either induces apoptosis and reduces proliferation of MYC-over- the death of cells expressing higher levels of Omomyc, expressing cells in the absence of side effects in normal tis- counterselection of the Omomyc transgene, or a combina- sue (Soucek et al. 2004). However, the therapeutic impact tion of both. of Omomyc in melanoma has not been studied yet. Western blot (WB) analysis with an anti-Omomyc anti- Hence, here we modeled MYC inhibition in melanoma body revealed that Omomyc expression levels are highly using a panel of cell lines of human origin with different correlated with cell number reduction (r 2 = 0.278; P = driver mutations and assessed both in vitro proliferation 0.0056) (Supplemental Fig. S1E). Thus, as previously shown and in vivo tumor growth in the presence and absence of in triple-negative breast cancer (TNBC) (Massó-Vallés et al. Omomyc. We also investigated the effect of MYC inhibi- 2022), Omomyc’s ability to reduce proliferation depends on tion on melanoma metastatic capacity and transcriptional its expression levels and appears to be independent of the profile, relating Omomyc-induced changes to transcrip- mutational profile of the cell lines. tional data from melanoma patients. Our results indicate To further characterize the antiproliferative effect that specific gene sets reflecting decreased MYC tran- caused by MYC inhibition, we performed a cell cycle 304 GENES & DEVELOPMENT MYC-dependent prognostic signature in melanoma A B C D E F Figure 1. Omomyc induces growth arrest in melanoma human cell lines in vitro. (A) Growth inhibition by transgenic Omomyc at 10 d by Crystal Violet staining. (B) Early effect of Omomyc on proliferation by cell count. (C) Colony formation assay. (D) Proliferation and cell death following Omomyc expression as a late event in A375 (top panels) and SkMel147 (bottom panels). (E) Cell cycle profiles during the first 4 d of Omomyc expression of A375 and SkMel147. Student’s t-test of Omomyc-expressing samples to their relative controls (n = 3). (E) Relation between Omomyc expression and inhibition of proliferation. (F ) MYC and Max expression at 7 d of Omomyc expression (repre- sentative image from triplicates). (∗ ) P < 0.05, (∗∗ ) P < 0.01, (∗∗∗ ) P < 0.001, (∗∗∗∗ ) P < 0.0001. analysis at 1, 2, 3, and 4 d of expression of Omomyc in we performed WB analysis after 1 wk of induction. Inter- A375 and SkMel147, two cell lines with different onco- estingly, while we found that Omomyc caused a decrease genic drivers: BRAFV600E and NRASQ61K, respectively. in MYC levels in nearly all cell lines, with the exception of Despite these differences, we observed a very similar A375 (lower expression but statistically nonsignificant), change in their cell cycle profile as a consequence of Omo- SkMel37 (higher, though not significant), and SkMel173 myc expression already since day 1: a significant increase (no change), the modulation of Max was less consistent: in G0–G1 populations that peaked above 80% on day 4, Lower levels were observed in A375 and 501Mel, and with a concomitant and steady decrease in the proportion higher levels were observed in SkMel173 (Fig. 1F; of cells in S phase, down to a marginal 5%, and G2–M quantified in Supplemental Fig. S1G). Additionally, we phases to even lower levels, with almost no changes in found a significant correlation between relative MYC lev- sub-G1 populations (Fig. 1E; Supplemental Fig. S1F). els and proliferation (r 2 = 0.535, P = 0.0002) (Supplemental Taken together, these results show that Omomyc in- Fig. S1H). duces an increase in the percentage of cells in G1 already at day 1 that results in evident in vitro growth arrest at day 3, which is maintained for at least 4 wk. Omomyc expression induces extensive transcriptional modulation in melanoma cells Given the role of MYC as a master regulator of gene ex- Omomyc expression reduces MYC levels in different pression, we analyzed the transcriptional effect of MYC melanoma cell lines inhibition by Omomyc. In order to do so, we plated To determine whether Omomyc had any effect on MYC BRAFV600E mutated A375 cells in the presence or absence and Max protein levels in the different cellular contexts, of doxycycline and performed a microarray analysis at 4 d, GENES & DEVELOPMENT 305 Zacarías-Fluck et al. when Omomyc expression levels had reached their gether and had a gene expression pattern distinct from steady-state levels (Fig. 2A), to look at transcriptional that of the vehicle counterparts (Fig. 2B). Specifically, changes that coincided with the antiproliferative effect 3203 genes were down-regulated by Omomyc expression of Omomyc. As expected, principal component analysis (17.25%), while 3875 were up-regulated (20.87%) (Fig. showed that Omomyc-expressing samples clustered to- 2C; Supplemental Table S1A), with an overall modulation A B D C E F G H I Figure 2. Omomyc induces transcriptional modulation in the A375 melanoma human cell line in vitro. (A) Omomyc-RFP levels deter- mined by cytometry, with percentage of positive cells and mean intensity. (B) Principal component analysis showing the separation of the five Omomyc-expressing (Dox) replicates from the control ones. (C ) Heat map of A375 cells expressing Omomyc or not at day 4 (absolute log FC > 2.25). (D) Volcano plot of A375 cells expressing Omomyc. (E) qPCR validation of microarray results of selected genes at 6, 12, 24, and 96 h of Omomyc expression. (F ) Heat maps of the top 10 regulated genes in selected gene sets. Representative MYC-related (G) and melanoma and other skin cancer (H) gene sets modulated by Omomyc. (I) Immune-related gene set enrichment induced by Omomyc expression. 306 GENES & DEVELOPMENT MYC-dependent prognostic signature in melanoma of expression of ∼40% of the genes (Fig. 2D, heat map for A the top 152 genes with absolute log FC > 2.25). To delve into Omomyc’s mechanism of transcriptional modula- tion, we performed qPCR analysis of five genes as early as 6 h, 12 h, 24 h, and 4 d after Omomyc expression, which is the same time point of the microarray. The transcrip- B tional changes were only significant at 4 d, with the excep- tion of HSPD1, which already showed a small but significant decrease at 24 h (Fig. 2E). Of note, we believe that the variability of the results at 6 and 12 h reflects the low and incomplete expression of Omomyc in the cells, which instead reaches much higher levels at 24 h, when the gene expression levels are much less variable. In line with this massive change in gene expression, gene set enrichment analysis revealed a plethora of differ- ent transcriptional programs affected by Omomyc (Fig. 2F–I; Supplemental Table S1B,C). In fact, and intimately related to MYC’s role in tumor progression (Kress et al. 2015), MYC and E2F targets as well as cell cycle check- point gene sets were significantly down-regulated (Fig. 2F,G). Interestingly, MYC inhibition by Omomyc expres- sion also led to a metabolic reprogramming, with a signifi- cant decrease in the oxidative phosphorylation gene set, a pathway notoriously associated with invasion of primary melanoma (Fig. 2F; Salhi et al. 2020). Also directly related to melanoma, Omomyc impaired the expression of several gene sets relevant to its progres- sion and up-regulated genes normally found underrepre- sented in melanoma metastasis (i.e., Alonso metastasis C down and Schlingemann skin carcinogenesis TPA down) (Fig. 2H). Finally, in line with MYC’s role in immune suppres- sion, MYC inhibition led to a proinflammatory expression profile (Fig. 2I). Figure 3. Omomyc induces a dramatic growth arrest in melano- Hence, in vitro expression of Omomyc induces broad ma human cell lines in vitro. Changes in the expression of genes transcriptional reprogramming, not only affecting MYC related to cell cycle and division in A375 cells at the mRNA (A) targets and MYC-related transcriptional programs but and protein (B) levels after 7 d of OmomycRFP expression. (C ) also down-regulating key genes involved in melanoma Mutations in TP53 gene (cBioPortal). metastases, invasion, and relapse. cell lines included in the panel bear a total of five muta- MYC inhibition by Omomyc leads to cell cycle arrest tions in the TP53 gene; of these four are in the DNA through down-regulation of multiple targets binding domain (L145R, R175H, E258K, and C277W) In line with the results of the cell cycle analyses, gene ex- and one is a truncating mutation before the tetrameriza- pression analysis of A375 showed that most of the changes tion domain (Q317) (Fig 3C; Supplemental Table S2). In- in cell cycle-related genes are present at 4 d of Omomyc terestingly, we found a marked and significant down- expression (Fig. 3A). Intriguingly, at this time point, Omo- regulation of the p53 mutant forms in three out of four myc induced down-regulation of TP53 mRNA levels, yet p53 mutant cell lines. Moreover, p21 levels increased its downstream target CDKN1A (Jalili et al. 2012), a key in two of these cell lines (SkMel37 and MeWo), pointing cell cycle inhibitor, was up-regulated. to a p53-independent increase, maybe through inhibition We then extended this cell cycle-related analysis to of MYC-Miz-1-induced repression (Wu et al. 2003) or the remaining human melanoma cell lines in our panel, Akt phosphorylation (Shamloo and Usluer 2019). Thus, performing WB at 7 d of Omomyc expression (Fig. 3B). At Omomyc induces cell cycle arrest with concomitant sta- this later time point, Omomyc led to a clear and signifi- bilization of wild-type p53 and down-regulation of mu- cant stabilization of p53 in most wild-type p53 cell lines tant p53 that leads to increased levels of cyclin- with a concomitant and significant increase in the dependent kinase inhibitor p21. levels of p21 protein, including in A375, where TP53 We then analyzed CyclinB1, which is necessary for the mRNA levels were decreased, and with the exception control of G2/M transition (Wang et al. 2014), and Cyclin of SkMel173, in which p21 levels were lower in Omo- D1 and Cdk4, both of which are involved in G1/S transi- myc-expressing cells than in control cells. Four of the tion (Diehl 2002). We found a general down-regulation of GENES & DEVELOPMENT 307 Zacarías-Fluck et al. all three proteins, although, intriguingly, the extent of Notably, after only 3 d of Omomyc expression, the per- down-regulation is very variable across the cell lines. centage of Ki67+ cells in the invasive front of the tumor Finally, we analyzed MCM5 and MCM6, two compo- had significantly decreased in A375 (P = 0.0123) (Fig. 4C), nents of the minichromosome maintenance complex as did the tumor volume (Supplemental Fig. S3F, left pan- (MCM), the replicative helicase essential for the DNA rep- el). In the case of SkMel147, due to their high growth rate lication during cell cycle and DNA repair (Costa et al. and the slow kinetics of Omomyc expression triggered by 2011), and again found a very clear decrease in their levels doxycycline, by day 3 the median volume of the treated in all of the cell lines. Similarly, we found decreased levels group was already 264.6 mm3 (IQR: 192.4–517.4 mm3, of PCNA, a cofactor of DNA polymerase δ (Moldovan similar to the vehicle group) (Supplemental Fig. S3F, right et al. 2007), and saw remarkable down-regulation of panel). Nonetheless, despite its delay, the percentage of MAD2L1, a mitotic spindle assembly checkpoint compo- Ki67+ cells was at that point lower in mice drinking nent and MYC target (see Supplemental Fig. S2 for all of doxycycline (P = 0.0176) (Fig 4D), anticipating the final re- the quantifications; Menssen et al. 2007). sult: a durable tumor regression induced by Omomyc In conclusion, Omomyc induces cell cycle arrest expression. through down-regulation of several key genes—and their A similar effect of inhibition of growth and regression is respective protein products—controlling cell cycle and achieved by Omomyc also in a p53 mutated context of hu- DNA replication, irrespective of the melanoma driving man melanoma cell lines (SkMel28 and SkMel37, bearing mutation or p53 status. mutations in both p53 and BRAFV600E) (Supplemental Fig. S3G,H). Hence, Omomyc expression in melanoma cells in vivo Omomyc induces tumor regression in human melanoma is sufficient to induce a rapid and durable tumor regres- CDXs sion even in advanced tumors, independently of their Encouraged by these in vitro results, we decided to assess TP53 status. the effect of Omomyc expression in vivo, making use of the two melanoma cell lines that we picked as representa- Omomyc curbs metastases in human melanoma CDXs tive of BRAF mutated and NRAS mutated melanoma: and prevents postsurgery tumor recurrence A375 and SkMel147, respectively. We inoculated these cells subcutaneously into immunocompromised mice, Melanomas are intrinsically metastatic, and this is the and when the mean tumor volume reached 100 mm3, main cause of death among patients. Thus, after confirm- mice were randomized into vehicle and doxycycline groups ing the outstanding effect of Omomyc on primary tumor (Supplemental Fig. S3A). Systemic administration of doxy- growth in vivo, we looked for any effect on metastases cycline in the drinking water did not affect mouse weight, by ex vivo imaging of specific organs. Not surprisingly, although we observed an initial slight decrease in both we found metastatic colonization of the lungs, liver, and models, most probably due to animal adjustment to the lymph nodes of the control mice (Fig. 4E). Encouragingly, change in taste of the drinking water (Supplemental Fig. instead, Omomyc significantly impaired the metastatic S3B). Strikingly, Omomyc caused dramatic regression of spread of BRAF mutant A375 cells in vivo, decreasing A375 tumors (Fig. 4A, left panel; Supplemental Fig. S3C) the percentage of mice with metastases (P < 0.001 in all and significantly prolonged mouse survival (Fig 4A, right cases) (Fig. 4E). Notably, we observed an even more dra- panel) until the end of the experiment (4 wk), when most matic result in NRAS mutant SkMel147, where Omomyc of the control mice had to be euthanized due to maximum completely inhibited the metastatic spread (P < 0.001 in tumor volume (P < 0.0001) (Fig. 4A; Supplemental Fig. S3C). all cases) (Fig. 4F). In this case, when we found residual A similar striking effect was observed in SkMel147 tumors: metastases under doxycycline treatment, these did not ex- Here, there was a first phase of tumor stabilization followed press Omomyc anymore, meaning that they were actual by tumor regression. Again, also in this case, there was a escapees that had lost the expression of the transgene significant increase in mouse survival, with all of the vehi- (Supplemental Fig. S3I) and were not considered in the cle-treated mice needing euthanasia by day 15, whereas all analysis. of the mice bearing Omomyc-expressing tumors survived Finally, to test the effect of Omomyc in the adjuvant at least to the end point, established at 26 d, corresponding setting, we inoculated A375 and SkMel147 cells into to double the survival time of control mice (P < 0.0001) (Fig. nude mice and let the tumors grow until 500 mm3, at 4B; Supplemental Fig. S3D). It is noteworthy that tumors of which point the primary lesions were surgically removed mice drinking doxycycline did eventually start to grow lat- and mice were randomized to receive either doxycycline er in the experiment. This is due to outgrowth of cells lack- or vehicle in the drinking water with periodical IVIS ing expression of Omomyc, as shown in Supplemental scanning to detect metastases (Supplemental Fig. S3J). Al- Figure S3E. This could be a consequence of negative selec- though A375-bearing mice showed a very low percentage tion, leading to the complete loss of the transgene (which of tumor recurrence and lung metastases after tumor re- seems to be the case in A375 cells), or the selective prolifer- section, tumors recurred only in control mice but not in ative advantage of cells expressing the lowest—or null— those under doxycycline treatment, with a concomitant levels of Omomyc (like in SkMel147, where very faint nonsignificant reduction of lung metastases in treated RFP levels can be detected in some cells that, importantly, mice (P = 0.068) (Fig. 4G). This was also confirmed in the still lack Ki67 expression). SkMel147 model with even more striking results, as 308 GENES & DEVELOPMENT MYC-dependent prognostic signature in melanoma A C B D E G F H Figure 4. Omomyc induces tumor regression and inhibition of metastases in human melanoma cell lines in vivo. Omomyc significantly impairs the growth of A375 (A) and SkMel147 (B), increasing the survival of tumor-bearing mice. Ki67 staining after 3 d of Omomyc ex- pression in the tumor margin of A375 (C ) and SkMel147 (D). Percentage of mice with spontaneous lung, liver, and lymph node metastases in A375 (E) and SkMel147 (F ) models. Recurrence and lung metastases after tumor resection in A375 (G) and SkMel147 (H). (Veh) Vehicle, (Dox) doxyclycline. tumors recurred in >50% of control mice but not in Omo- wk of Omomyc expression in A375 and SkMel147 myc-expressing ones, which survived to the end point subcutaneous tumors and subjected them to microarray with significantly fewer metastases (P < 0.001) (Fig. 4H). analysis (Supplemental Fig. S4A). For this study, we spe- Taken together, these data show that Omomyc reduces cifically selected tumors of similar sizes in order to avoid melanoma cell proliferation, causes tumor regression in any gene expression change due to this variable (Supple- vivo, reduces the metastatic capacity of melanoma cells, mental Fig. S4B, bottom panels). Principal component and prevents tumor recurrence when expressed after sur- analysis showed that Omomyc-expressing tumors from gical removal of the primary tumor. both cell lines have a transcriptional profile that is distinct from their vehicle counterparts (Supplemental Fig. S4C). Despite some variability in gene expression among tu- Omomyc-induced tumor regression in vivo is mors (PC2 axis in Supplemental Fig. S4C), we were able accompanied by transcriptional modulation to identify a large cadre of genes significantly regulated In order to gain more insight into the mechanism of action by Omomyc in both cell line-derived tumors (Supplemen- leading to therapeutic impact of MYC inhibition by Omo- tal Fig. S4D; Supplemental Table S3A,B). Supplemental myc in melanoma in vivo, we took tumor samples after 1 Figure S4E shows regulation of the top 200 genes, from GENES & DEVELOPMENT 309 Zacarías-Fluck et al. which we selected and validated some by qPCR (Supple- both MYC and melanoma biology: genes involved in mel- mental Fig. S4F). anoma progression and metastases, MYC targets, cell cy- Despite the fact that A375 and SkMel147 are driven cle and division, DNA structure and replication, RNA by different oncogenic mutations (BRAFV600E and processing and transcription, and DNA repair and metab- NRASQ61R, respectively), we found considerable overlap olism. In fact, the majority (75.7%) of the regulated genes in transcriptional modulation by Omomyc: 51% of belong to at least one of these categories (Fig. 5B; Supple- down-regulated genes (DN) in A375 were also down-regu- mental Table S5A,B). lated in SkMel147, whereas 26% of the up-regulated genes Confirming the specificity and therapeutic relevance of (UP) in A375 showed the same modulation in SkMel147 MYC inhibition in vivo, gene set enrichment analysis re- (Fig. 5A; Supplemental Table S4). We found a clear link be- vealed a conserved collection of gene sets comprising both tween the 436 DN genes and gene categories relevant for pathways related to melanoma and MYC that were A B C D H E Figure 5. Omomyc-induced transcriptional modulation independent of melanoma driver mutation targets key melanoma genes. (A) Overlaps of modulated genes by Omomyc in vivo. (B) Chord diagram of common down-regulated genes linked to categories of down-reg- ulated gene sets. (C ) Selected common down-regulated gene sets. (D) Supervised K-means clustering string analysis of down-regulated genes. (E) Transcription factor binding site profiles in the ENCODE and ChEA libraries. 310 GENES & DEVELOPMENT MYC-dependent prognostic signature in melanoma significantly down-regulated by Omomyc (summarized in metastases on day 18. Mice were randomized, and doxycy- Fig. 5C; Supplemental Table S6). Notably, the heat maps cline was administered. After 2 d, mice were scanned from the melanoma metastasis and relapse and the again, and metastases were confirmed in eight of the 10 MYC target gene sets show that the vast majority of the initial mice (Supplemental Fig. S6A). Despite the extreme genes in these categories are down-regulated by Omomyc transcriptional variability observed among the metastases (Supplemental Fig. S4G). from vehicle-treated mice (Supplemental Fig. S6B), we In addition, unsupervised string analysis of common found several gene sets modulated by Omomyc, both DN genes groups them into four different clusters (Supple- down-regulated (DN) and up-regulated (UP) (Supplemen- mental Fig. S5A; Supplemental Table S7). When we ana- tal Fig. S6C; Supplemental Table S10). Although the tran- lyzed these clusters, we found that they encompassed scriptional analysis of primary tumors was performed at 7 genes involved in (1) chromatin modification, organiza- d while that of lymph node metastases was only at 2 d, al- tion, and remodeling, as well as transcription; (2) cell me- most 50% of the DN gene sets were common to the two tabolism; (3) cell cycle; and (4) RNA processing and settings (Supplemental Fig. S6C). Among the DN gene metabolism (Fig. 5D; Supplemental Table S8A–D). Analo- sets, we found several that could at least partially explain gously, common UP genes show two clusters after unsu- the remarkable effect of Omomyc on metastases (Supple- pervised string analysis (Supplemental Fig. S5B; mental Fig. S6D): Besides general transcriptional pro- Supplemental Table S8E,F), including genes involved in grams such as translation and DNA replication, we (1) metabolism, lysosomes/autophagy, vesicle-mediated found the expected MYC target and melanoma metastasis transport, subcellular organization, and immune re- gene sets. Interestingly, EZH2 targets were also down-reg- sponse, and (2) immune response (Supplemental Fig. S5C). ulated. The enhancer of zeste homolog 2 (EZH2) is a his- In order to gain more insight into the regulation of the tone-lysine N-methyltransferase whose inhibition has DN genes, we subjected them to Enrichr analysis (Chen been shown to impact the invasive capacity of NRAS mu- et al. 2013; Kuleshov et al. 2016; Xie et al. 2021). Specifi- tant cells, like the SkMel147 used in this experiment (Ter- cally, we queried which transcription factor (TF) binding ranova 2021). Notably, some of the gene sets whose sites were present in the promoter region of these genes expression was exclusively abrogated in metastases are in- in the ENCODE and ChEA libraries. As expected, we found timately related to melanoma metastases. The most that the TFs most significantly represented in MYC-in- prominent ones are lamellipodium membrane, notch sig- duced DN genes in vivo presented MYC, Max, and E2F naling, and the ILK and ATF2 pathways. Indeed, lamelli- TF binding sites (Fig. 5E; Supplemental Table S9A). Inter- podia, together with filopodia, contributes to invasion estingly, when these genes were compared with kinase and metastases of tumor cells (Machesky 2008), while and GPCR perturbation libraries or Gene Expression Omni- melanoma metastases are promoted by Notch signaling bus (GEO) drug perturbation pathways, we found that the through its interaction with β-catenin (Bedogni 2014) most significantly similar profiles were those associated and by the transcription factor ATF2 by the suppression with knockdown of tyrosine and serine/threonine kinases of protein fucosylation (Lau et al. 2015). Finally, integ- involved in cell cycle, proliferation, and transcriptional reg- rin-linked kinase (ILK) is a serine/threonine kinase impor- ulation of genes—among others—in melanoma (Supple- tant for melanoma cell migration through the formation mental Fig. S5D; Supplemental Table S9B), or inhibition of actin cytoskeleton and motility structures and in the of RTK–AKT–MAPK pathway components and cell cycle signal transduction upon engagement to ECM attach- regulators in cancers of different origins (Supplemental ment (Wong et al. 2007). Fig. S5E; Supplemental Table S9C). These analyses clearly Among the few up-regulated gene sets found in LN me- show that MYC, Max, and E2F are the most prominent tastases from mice under doxycycline treatment, we TFs regulating the expression of DN genes and that Omo- found one reinforcing the inhibition of ATF2 activity myc-expressing tumors phenocopy melanomas with com- (ATF2 UP.V1 DN; genes down-regulated by ATF2 when promised key signaling pathways. hyperactive, also present in the primary tumor after 7 d) Overall, these results obtained through different and three gene sets consisting of genes down-regulated approaches show that Omomyc induces a severe transcrip- during skin cancer progression, specifically found in me- tional modulation in vivo—conserved between melanomas tastases: hummerich skin cancer progression DN, benign with different driver mutations—that affects not only sin- skin tumor DN, and malignant skin tumor DN. gle genes but entire transcriptional programs involved in Taken together, these results confirm that Omomyc tar- melanoma progression and metastasis, as well as chroma- gets not only the proliferation and survival of primary and, tin modification, metabolism, and, as expected, a variety by extension, metastatic tumor cells but also different of MYC-related programs relevant for tumorigenesis. mechanisms directly involved in the metastatic spread. Finally, to take a closer look at the metastatic process and early transcriptional changes that could account for Melanoma patients classified by an Omomyc-derived the outstanding effect of Omomyc, we performed a micro- six-gene signature show improved OS, decreased MYC array analysis of lymph node metastases after only 2 d of activity and metastatic capacity, and increased immune doxycycline treatment (before their quick disappearance response caused by Omomyc), which is equivalent to ∼1 d of subop- timal Omomyc expression. We inoculated SkMel147 cells The ultimate goal of our MYC inhibition study is to pre- subcutaneously and scanned the mice for spontaneous dict how such a therapeutic approach could potentially GENES & DEVELOPMENT 311 Zacarías-Fluck et al. impact patient treatment and/or survival and how Omo- hypoxia score (P < 10−10), and a lower fraction of altered ge- myc-induced transcriptional reprogramming relates to nomes (P = 3.884 × 10−4) (Fig. 6A,B). melanoma patient prognosis. To this end, we accessed This six-gene signature discriminated patients with dif- TCGA data of melanomas with RNA-seq and clinical in- ferential gene expression, with a plethora of genes down- formation (Hoadley et al. 2018) and analyzed how differen- regulated and up-regulated in patients with good progno- tial expression of every single DN or UP gene in both sis with respect to bad prognosis (summarized in Fig. 6C). BRAFV600E- and NRASQ61L-driven melanomas impacts In more detail, 63% of genes up-regulated by Omomyc patient survival. Out of 436 commonly DN genes, lower in vivo are enriched in patients with good prognosis, than the median expression of 143 of them (32.8%) is sig- whereas 56% of genes down-regulated by Omomyc are en- nificantly associated with increased overall survival (OS) riched in poor prognosis patients (Fig. 6D), pointing to a in melanoma patients. Similarly, high expression of 70 reasonable predictive value of the results obtained with out of 156 UP genes (44.9%) in Omomyc-expressing tu- Omomyc in a clinical setting. mors is associated with increased OS in patients (Supple- In order to analyze how these data related specifically to mental Table S11). Next, in an attempt to see whether MYC biology and melanoma, we performed preranked Omomyc’s transcriptional signature could be beneficial GSEA in patients and found that good prognosis patients to patients, we focused on genes associated with better have lower expression of MYC target genes as well as of prognosis. With this filter, we generated a hybrid six- genes involved in melanoma metastases and relapse (Fig. gene signature encompassing BIRC5, CCNB2, and 6E). Consistently, these patients also showed significantly ORC1 from the DN genes and DDX60, PARP9, and lower expression of genes involved in epithelial-to-mesen- DTX3L from the UP genes. As expected, patients with chymal transition, E2F targets, and cell cycle (Fig. 6F), as this signature have significantly increased OS (205 mo well as increased expression of adaptive and inflammatory [162–NA mo] vs. 42 mo [28–63 mo], P = 7 × 10−7), a lower immune response genes (Fig. 6G). Finally, in line with A B C D E F G H Figure 6. Melanoma patients stratified by a six-gene signature show improved OS with lower metastatic capacity and increased immune re- sponse. (A) Survival of melanoma patients discriminated by a six-gene signature. (B) Patients with good prognosis have a lower hypoxia index and a lower fraction of the genome altered. (C) Volcano plot depicting gene expression of good prognosis versus poor prognosis patients. (D) Overlap between Omomyc-modulated genes in preclinical settings (percentage shown) and genes significantly enriched in good and poor prog- nosis patients. Preranked GSEA shows decreased MYC transcriptional activity, metastatic capacity, and melanoma relapse (E) and decreased expression of EMT genes, E2F targets, and cell cycle genes (F). (G) Tumors from patients with good prognosis show enrichment of genes related to immune response. (H) There was no evidence of differential MYC transcriptional repression activity in either group of patients. 312 GENES & DEVELOPMENT MYC-dependent prognostic signature in melanoma what we had seen in vivo, we found no differences in the tors CyclinB1, CyclinD1, and MYC target Cdk4 (Hermek- expression of genes repressed by MYC (Fig. 6H), which ing et al. 2000). However, progression through the cell may reflect the fact that these genes do not have a clear cycle is not only regulated by cyclins and cyclin-depen- impact on melanoma patient survival. dent kinases but also involves, among other processes, the replication of DNA and cell division. For example, minichromosome maintenance proteins (MCMs) are Discussion part of DNA helicase complexes that help unwind DNA during its replication, recombination, and repair (Seo MYC’s role in tumor progression and maintenance has and Kang 2018). In addition, proliferating cell nuclear an- long been established for most human cancers. In melano- tigen (PCNA) is a cofactor of DNA polymerase δ and, as a ma, it has been shown to be deregulated by amplification of tetramer, helps increase the processivity of leading strand the 8q24 chromosome or by upstream activating mutations synthesis during DNA replication (Moldovan et al. 2007). in the RAS/RAF/MAPK pathway, which results in an ag- Finally, the mitotic spindle assembly checkpoint main- gressive clinical course and resistance to targeted therapy. tains genome stability during chromosome segregation To date and to our knowledge, direct inhibition of MYC before cell division (Lara-Gonzalez et al. 2012), and one in melanoma has only been tested using small interfering of its key components is MAD2L1, which is a direct RNA (siRNA) (Hong et al. 2007) or antisense oligonucleo- MYC target (Menssen et al. 2007). Remarkably, Omomyc tides directed against MYC (Dinçer et al. 2010; Massó- expression induced a striking down-regulation of all of Vallés and Soucek 2020), where it induced inhibition of them (MCM5, MCM6, MAD2L1, and PCNA). proliferation through cell cycle arrest, senescence, and ap- p53 is probably the best-studied tumor suppressor and is optosis, or using nanoparticles (NPs) containing the MYC key in preventing malignancy. It is capable of controlling inhibitor 10058-F4 in a 72-h proliferation assay, where the expression of a large number of genes, including those Alamar Blue metabolic testing showed a fourfold decrease involved in the cell cycle by p21-dependent indirect tran- in the proliferation of C32 BRAF mutant human melano- scriptional repression (Engeland 2022). cBioPortal data of ma cells (Pan et al. 2015). 1847 melanoma patients from eight different studies Here we made use of Omomyc, the most-characterized show that p53 or its inhibitors MDM2 and MDM4 are al- MYC inhibitor to date, which has just successfully com- tered in 27% of samples analyzed in most of cases corre- pleted a phase I clinical trial, to assess the therapeutic im- sponding to putative drivers (cBioPortal, https://bit.ly/ pact of MYC inhibition in melanoma both in vitro and in 3K2Hra9). The panel of cell lines used in this study con- vivo in the presence of different mutational profiles. Omo- tained five cell lines with wild-type and four cell lines myc had previously shown an outstanding antitumori- with mutated p53. Interestingly, while in p53 wild-type genic effect in a wide range of murine preclinical models cells Omomyc induced stabilization of the protein, in mu- of cancer, without any severe side effects in normal tis- tant cells it caused a significant reduction in the expres- sues (Massó-Vallés and Soucek 2020). Most recently, it sion of their mutated isoforms. In most of the cases, this has also been shown to target and curb the metastatic modulation was accompanied by an increase in cyclin-de- spread in TNBC models (Massó-Vallés et al. 2022). For pendent kinase inhibitor p21. Hence, by decreasing the these reasons and for the undisputed role of MYC in mel- level of multiple proteins involved in the progression of anoma, we sought to model MYC inhibition in this dis- the cell cycle and increasing at least one cell cycle inhib- ease and investigate its effects on progression and itor, Omomyc counteracts one of MYC’s fundamental metastases, the main causes of death for melanoma pa- roles in tumor progression. tients. Using a panel of human cell lines with different To gain further insight into the mechanism of action of driving mutations, we were able to show that Omomyc Omomyc in melanoma, we performed a microarray anal- expression in vitro has a significant impact on prolifera- ysis of BRAF mutant A375 melanoma cells in vitro. As tion and colony formation of the different lines, and this previously found in fibroblasts, non-small cell lung can- effect correlates with Omomyc expression levels. Al- cer, and TNBC (Savino et al. 2011), Omomyc expression though the induction of apoptosis after 1 wk of Omomyc abrogated several transcriptional programs, including expression is only evident in one-third of the cell lines, MYC and E2F targets, RNA metabolism, cell cycle, ribo- prolonged analysis up to 4 wk showed that, after a period some biogenesis, and RNA polymerase II-mediated tran- of stable growth arrest, cell death increases significantly scription. Furthermore, it up-regulated genes that are in different cellular contexts. normally repressed by MYC. Thus, Omomyc is able to In regard to cell cycle, MYC overexpression has been re- counteract MYC’s protumorigenic transcriptional activi- ported to enable cancer cells to enter it by inducing the ex- ty by reverting its ability to activate and, at least in vitro, pression of cell cycle regulators and inhibiting cell cycle repress gene expression. checkpoints (Dhanasekaran et al. 2022). When we per- To complement these studies with an in vivo analysis, formed cell cycle analysis in BRAF mutant A375 and we tested Omomyc’s ability to affect tumor growth in two NRAS mutant SkMel147 melanoma cells expressing different xenograft models, driven by BRAFV600E and Omomyc, we found consistent results: accumulation of NRASQ61R, respectively. In line with the in vitro results, cells in G1 phase with a concomitant remarkable decrease there was a profound inhibition of tumor growth and of cells in S and G2/M phases. We also found a significant even regression in both cases. Most remarkably, Omomyc decrease in the expression levels of the cell cycle regula- also curbed the metastatic spread, as analyzed in the GENES & DEVELOPMENT 313 Zacarías-Fluck et al. lymph nodes, lungs, and liver. Furthermore, it completely could extend to melanoma. Hence, in this work, we also prevented tumor recurrence after surgery and, again, abro- analyzed whether Omomyc-induced transcriptional mod- gated the appearance of late metastases. Importantly, in ulation in our preclinical models could relate to patient many of the residual metastases found in mice drinking outcome. First, we assessed whether the expression of doxycycline, we saw no sign of Omomyc expression, con- Omomyc-modulated genes could be related to survival firming that at least a proportion of these metastases was and found that approximately half of them confer survival the result of Omomyc transgene loss, a phenomenon al- advantage when their expression in patients is lower than ready observed before in transgenic mouse models of the median. In contrast, half of the UP genes confer sur- Omomyc (Soucek et al. 2013). These results were also vival advantage when their expression is higher than the confirmed in BRAF and p53 double-mutant cell lines. median. Thus, Omomyc not only is able to halt primary tumor With the aim of extrapolating a useful predictive signa- growth but, importantly, also targets the metastatic ture from our preclinical data to be used in melanoma pa- spread both in the presence of the primary tumor and as tients, we generated a hybrid gene signature that would be adjuvant after tumor resection. able to discriminate patients with good and poor progno- It should be noted that our experiments have been con- ses. This signature includes low expression of three genes ducted in immune-deficient models. Given the clear role with a known role in cancer progression (although only of MYC in immune suppression (Dhanasekaran et al. one of them has been thoroughly studied in melanoma): 2022) and the proinflammatory reprogramming that we Origin recognition complex subunit 1 (ORC1) is essential observed in A375 cells, our prediction is that Omomyc’s for the initiation of DNA replication and is tightly regulat- effect could be even more pronounced in an immunocom- ed during the cell cycle (https://www.ncbi.nlm.nih.gov/ petent context. gene/4998), BIRC5/Survivin prevents apoptotic cell Despite their different driver mutations, microarray death, and CCNB2 is a key component of the cell cycle analyses of in vivo tumors showed a significant overlap regulatory machinery and a potential prognostic predictor of modulated genes. Up to half of the down-regulated for melanoma (Chen et al. 2020). The signature also in- genes in one cell line were also down-regulated in the oth- cludes three genes with high expression, with limited er, while for up-regulated genes this overlap was ∼25%. In study in cancer to date and even scarcer in melanoma: order to determine the relevance of this in vivo transcrip- DDX60 is a putative RNA helicase implicated in cellular tional reprogramming of melanoma cells, we curated and processes involving RNA binding and alteration of RNA analyzed gene categories relevant to MYC and melanoma secondary structure (https://www.ncbi.nlm.nih.gov/ biology. Strikingly, up to 75% of the genes down-regulat- gene/55601), DTX3L is an E3 ubiquitin–protein ligase, ed by Omomyc belong to one of these categories, confirm- and the ADP-ribosyltransferase PARP9, together with ing the on-target effect and the biological relevance of DTX3L, plays a role in DNA damage repair (Takeyama Omomyc in melanoma. Different analytical approaches et al. 2003). Although DTX3L has been proposed to stim- (namely, string and gene set enrichment analyses) con- ulate melanoma metastases (Thang et al. 2015), patient firmed these results. data from cBioPortal (Liu et al. 2018) and Human Protein In addition, Enrichr analysis showed that the most prev- Atlas (https://www.proteinatlas.org/ENSG00000137628- alent transcription factor binding sites present in these DDX60) show that its expression is directly correlated common down-regulated genes are those for MYC and with increased patient survival. Max, along with E2F6. The two cohorts of patients classified by the signature We also show that Omomyc’s effect in melanoma phe- present radically different survival, with a fivefold in- nocopies the knockdown of components of the cell cycle crease for the good prognosis patients, who also show a or RAS–RAF–MAPK pathway as well as inhibition of dif- lower hypoxic index and fraction of the genome altered. ferent kinases, including mutant BRAF. Consequently, Notably, in melanoma, as in most solid tumors, hypoxia targeting MYC renders tumor cells incapable of prolifera- is associated with aggressiveness and metastases (Kalaora tion and survival in a manner similar to that attained with et al. 2022). The fraction of genome altered is the percent- tyrosine kinases inhibitors but without causing their un- age of genome that has been affected by copy number desired toxic effects and in the absence of potential signal gains or losses, and it has recently been shown that, in rewiring leading to resistance. Moreover, given that tu- melanoma, it increases in visceral metastases compared mors expressing Omomyc acquired a proinflammatory with their respective primary tumors (Papp et al. 2021). profile, it would be reasonable to think that combination Furthermore, transcriptomic analysis also shows that of Omomyc with IO would be beneficial for melanoma ∼60% of the genes up-regulated by Omomyc are enriched patients. Finally, further transcriptional analysis of spon- in good prognosis patients, while genes down-regulated by taneous metastases showed that Omomyc targets multi- Omomyc are enriched in poor prognosis patients, mean- ple mechanisms involved in the metastatic spread and ing that the transcriptional reprogramming observed in aggressiveness of melanoma. mice upon Omomyc expression in melanoma cells resem- The ultimate goal of our work for a very long time has bles that of patients with good prognosis. Accordingly, been to bring Omomyc to the clinical setting, a landmark gene set enrichment analysis showed a remarkable over- that was achieved in 2021, when it entered a phase I clin- lap of our preclinical data with data from patients classi- ical trial that was successfully completed in 2022. Now, fied according to the Omomyc-derived gene signature our current results suggest that its clinical applicability (low ORC1-–BIRC5–CCNB2 and high PARP9–DDX60– 314 GENES & DEVELOPMENT MYC-dependent prognostic signature in melanoma DTX3L): Expression of MYC and E2F targets, melanoma TECAN Spark spectrophotometer (Life Sciences). Protein ex- metastases and relapse, G2/M checkpoints, and epitheli- tracts were run on 10% or 12% precast gels (Life Technologies), al-to-mesenchymal transition gene sets is significantly transferred to nitrocellulose membranes (Millipore), and incubat- decreased, while adaptive and inflammatory immune re- ed with antibodies against c-Myc (Y69; Abcam ab32072), Cdk4 (D9G3E; Cell Signaling Technology 12790), CyclinD1 (92G2; sponse gene sets are enriched. In fact, the most biologi- Cell Signaling Technology 2978), Cyclin B1 (Cell Signaling Tech- cally relevant transcriptional programs are equally nology 4138), p53 (Dako M7001), p21 (clone CP74; Thermo Ab-11 modulated in Omomyc-expressing melanoma cells and MS-891-P0), Max (C-17; Santa Cruz Biotechnology sc-197 and in patients with good prognosis. R&D AF4304), MCM5 (EP2683Y; Abcam ab75975), MCM6 (H- In summary, the data presented here demonstrate that 8; Santa Cruz Biotechnology sc-393618), MAD2L1 (Sigma-Al- MYC inhibition is an effective therapeutic approach drich HPA003348), PCNA (BioLegend 307904), β-Actin (Sigma- against melanoma for both primary tumors and metasta- Aldrich A-5441), or Omomyc (noncommercial primary rabbit ses, able to reshape the tumor transcriptional landscape polyclonal anti-Omomyc antibody affinity-purified and selected toward an immunogenic and less proliferative profile, against the MYC b-HLH-LZ epitope). ECL images were acquired erasing MYC’s transcriptional footprint independently using an Amersham Imager 600 and iBright CL1500 imaging sys- tem (Thermo Fisher). Protein expression was quantified using the of the melanoma driver mutation. Additionally, by relat- Fiji image processing package containing ImageJ (Schindelin et al. ing our preclinical models to patient data, we unveil a 2012). gene signature that discriminates patient prognosis and underlines the therapeutic potential that such an ap- proach could eventually have in the clinic in this dismal Propidium iodide (PI) and cell cycle analysis disease. Cell cycle was done as previously published (Massó-Vallés et al. 2022). Briefly, cells were treated with 1 µg/mL doxycycline upon plating or untreated and grown for 4 d, followed by trypsini- Materials and methods zation, two washes in PBS, and fixation in ethanol. Next, 5 × 105 cells were treated for 20 min with 25 mg/L PI, 10 µg/mL RNase, Cell lines and viral infections and 0.1% Triton X-100 in the dark. Navios (Beckman Coulter Life A375, 501Mel, SkMel28, SkMel103, and SkMel147 human mel- Sciences) and FACSCanto II (BD Biosciences) flow cytometers anoma cell lines were a kind gift from Dr Aroa Soriano (Vall d’He- were used for data acquisition, and FCS Express 4 software was bron Institute of Research [VHIR], Barcelona) while SkMel37, used for data analysis. SkMel131 (clone 1.36–1.5), SkMel173, and MeWo human mela- noma cell lines were a kind gift of Cristina Ruiz Herguido (Insti- Proliferation assay tut Hospital del Mar d’Investigacions Mèdiques [IMIM], Barcelona). All cell lines were routinely tested for mycoplasma For Crystal Violet staining, cells were plated at low density (be- contamination. Cells were grown in DMEM supplemented with tween 100 and 2000 cells, depending on the cell line) in 96-well 10% fetal bovine serum and 1% L-glutamine (all from Life Tech- plates in the presence or absence of 1 µg/mL doxycycline and in- nologies). A375 and SkMel147 were validated at High Technolo- cubated for 10 d. Next, media was removed from the wells, and gy Unit (VHIR). cells were fixed with 4% PFA for 12 min, washed twice, stained Omomyc was cloned into the pTRIPZ lentiviral vector (Open with Crystal Violet for 20 min, and washed twice. For quantifica- Biosystems and Thermo Scientific) to generate a doxycycline-in- tion, 10% acetic acid was added to the wells, and absorbance at ducible Omomyc-RFP expression cassette, and the shRNA se- 580 nm was read after 10 min. quences were removed (Annibali et al. 2014). For infections, For cell count, A375 and SkMel147 were plated in p60 plates in 293T cells were seeded at 70% confluence, and the following the presence or or absence of 1 µg/mL doxycycline and incubated morning, 25 mM chloroquine was added. Two hours later, 293T for 1, 2, 3, and 4 d in one experiment and for 1, 2, 3, and 4 wk in cells were transfected with pTRIPZ-Omomyc plus the lentiviral another experiment. Media and PBS from the washing were re- vectors pMD2G and psPAX2 by the CaPO4 method. The medium covered and mixed with the cells after trypsinization. Live and was changed the following day, and sodium butyrate was added dead cells were counted with Vi-Cell XR cell viability analyzer at 5 mM. Viral supernatants were harvested on the subsequent (Trypan Blue dye exclusion method). 2 d, filtered, and added to target cells with 0.8 µg/mL polybrene. All experiments were repeated in at least three independent ex- Retroviral infection using 293 cells transduced with pQCXIH- periments, and samples were prepared at least in triplicates. firefly luciferase vector was used to modify cells for in vivo experiments. Cell death assay To analyze the viability of the cells at 7 d of Omomyc expression, Western blots 15,000–130,000 cells, depending on the cell line, were plated in Western blots were performed as previously published (Massó- p100 plates and grown for 1 wk in the presence or absence of dox- Vallés et al. 2022), with modifications. Briefly, cells were plated ycycline. After trypsinization and recovery of cells, live and dead with or without 1 µg/mL doxycycline and grown for 4 or 7 cells were counted as above. d. Media was aspirated, cells were washed with PBS, and dry plates were stored at −80°C until processing. Frozen cells were Colony formation assay scraped with RIPA buffer supplemented with protease and phos- phatase inhibitors (Roche), incubated for 30 min, and centrifuged Cells were plated at low density (250–1000 cells, depending on at maximum speed for another 30 min. Supernatants were col- the cell line) in six-well plates in the presence or absence of dox- lected, total protein concentration was quantified by DC protein ycycline and incubated until the colonies in the control wells assay (Bio-Rad), and absorbance at 560 nm was measured with a started showing signs of cell death due to local overconfluence. GENES & DEVELOPMENT 315 Zacarías-Fluck et al. At the end point, media was removed from the wells, and cells tumor resection. Once the success of the surgery was confirmed, were fixed with 4% PFA for 12 min, washed twice, stained with mice were randomized into two groups as above; i.e., 5% sucrose Crystal Violet for 20 min, and washed twice. in drinking water with or without 2 g/L doxycycline. Mice were routinely checked for tumor recurrence and scanned in IVIS to detect metastases. qRT-PCR Quantitative reverse transcription polymerase chain reaction Transcriptional analysis of lymph node metastases (qRT-PCR) was performed as previously described (Massó-Vallés et al. 2022). Briefly, to mimic the microarrays conditions, A375 SkMel147 cells were inoculated as above, and mice were scanned and SkMel147 Omomyc-RFP cells were treated with 1 μg/mL for metastases when they reached ∼750 mm3. At this point, 10 doxycycline for 6, 12, and 24 h and for 4 d or left untreated. mice were randomized in vehicle and doxycycline groups as RNA was then extracted with TRIzol and quantified using Nano- above. Two days after treatment onset, mice were euthanized Drop. Equal amounts of RNA were reverse-transcribed to gener- by CO2 inhalation, and lymph nodes, lungs, and brains were ex- ate cDNA using iScript reverse transcription Supermix for qRT- cised and scanned to confirm the presence of metastases. Finally, PCR (Bio-Rad). SYBR Green qRT-PCR analysis was then per- eight lymph node metastases, four per group, were snap-frozen formed on these cDNA samples with PerfeCTa SYBR Green Fast- and stored until RNA was extracted. Mix and Low Rox (Quantabio) using the QuantStudio 6 FLEX system (Applied Biosystems). The data thus obtained were ana- lyzed following the comparative (ΔΔCt) method described by In vivo imaging Livak and Schmittgen (2001). Analysis of relative gene expression Metastatic colonization was monitored by in vivo biolumines- data was performed using real-time quantitative PCR and the cence imaging using the IVIS-200 imaging system from Xenogen 2−ΔΔC(T) method (Livak and Schmittgen 2001). Glyceraldehyde (Perkin Elmer) as previously described (Massó-Vallés et al. 2022). 3-phosphate dehydrogenase (GAPDH) and β-Tubulin were used Briefly, mice were injected intraperitoneally with a d-luciferin as housekeeping genes. Sequences of primers used are listed in solution (150 mg/kg in PBS) for 5–10 min prior to acquisition. Supplemental Table S12. Mice were anesthetized with isoflurane (5% for induction and 2% during acquisition), and air flow was set at 0.8 L/min. IVIS data were analyzed with Living Image software (Perkin Elmer). Animal studies Study analysis consisted of a light radiance quantification. Sig- All of the animal studies were performed in accordance with AR- nals from the light sources were detected and characterized. RIVE guidelines and following the three-R rule of replacement, Working units were photons/second/square centimeter/stera- reduction, and refinement principles. Mice were housed and dian, which allowed comparison between images obtained by dif- treated in accordance with protocols approved by the CEEA (Eth- ferent acquisition parameters. Acquisition and analysis were ical Committee for the Use of Experimental Animals) at the Vall carried out by the Preclinical Imaging Platform staff at VHIR. d’Hebron Institute of Oncology (VHIO), Barcelona. For the in vivo experiments, 12 mice were used per treatment group, and they were randomized by tumor size and weight. Immunohistochemistry and confocal microscopy The experimental end point was established a priori when tumor Paraffin-embedded tumor sections were cut at 4 µm thick and volume reached 1750 mm3, when metastases showed clear signs stained with H&E for pathological examination. For Ki-67 immu- of growth, or when mice showed any sign of discomfort. Experi- nofluorescence, antigen retrieval was performed by heating for 20 ments were not performed in a blinded fashion. min at 400 W in a microwave in 0.01 M citrate buffer (pH 6.0). Af- All of the schematics were created with Biorender.com. ter blocking for 45 min in 3% BSA and washing in PBS, slides were incubated overnight at 4°C with anti-Ki-67 (Thermo Scien- tific RM-9106-S0) and diluted 1:100 in Dako ready-to-use diluent Cell line-derived subcutaneous model (Dako S2022). After a PBS wash, slides were incubated with goat A375 (2 × 106), SkMel147 (2 × 106), SkMel37 (4 × 106), or SkMel28 antirabbit IgG (H+L)-Alexa Fluor 488 conjugate (Thermo Fisher (10 × 106) human melanoma cells were inoculated subcutaneous- Scientific A-11008) diluted 1:200, stained with DAPI (Life Tech- ly into the dorsal flanks of 6-wk-old athymic BALB/cAnN- nologies D1306) diluted 1:10,000, washed once with water, and Foxn1 nu/nu/Rj mice (Janvier Laboratories). Once the tumors mounted with fluorescence mounting medium (Dako S3023). reached 100 mm3, mice were randomized for treatment. One For anti-Omomyc immunohistochemistry, the same procedure group was given 2 g/L doxycycline in 5% sucrose in the drinking was performed, but a primary rabbit polyclonal anti-Omomyc an- water to induce expression of the Omomyc-RFP transgene in the tibody (affinity-purified and to remove MYC cross-reactive anti- melanoma cells, while the control group was given 5% sucrose. bodies) was used at a 0.02 mg/mL final concentration. Confocal At the end point, mice were euthanized by CO2 inhalation. Tu- microscopy images were captured using a Nikon C2+ confocal mors were then excised, fixed for 48 h in buffered 4% formalde- microscope and NIS-Elements software. Images were acquired hyde, transferred to 70% ethanol, and embedded in paraffin. and analyzed in a blind fashion. Tumor excision model Microarray analysis To analyze the effect of Omomyc in the metastatic capacity of the For RNA extraction from A375 cells, cells were seeded in the melanoma cell lines after the primary tumor was excised, cells presence or absence of doxycycline and incubated for 3 d (n = 5). were inoculated as above, and tumors were excised when they Plates were washed twice with PBS and frozen at −80°C until pro- reached 500 mm3. Mice were subjected to surgery and allowed cessing. For tumors, a viable section of tumors from control or to recover. Two days after, they were scanned to detect lucifer- treated mice (n = 5 for A375 and n = 4 for SkMel147) were snap-fro- ase-expressing tumor cells that could have been left behind dur- zen until processed. For lymph node metastases, ipsilateral axil- ing surgery in order to discard including mice with incomplete lary lymph nodes with confirmed metastases (n = 4 per group) 316 GENES & DEVELOPMENT MYC-dependent prognostic signature in melanoma were snap-frozen until processing. In all of the samples, RNA was Enrichr analysis extracted with TRIzol reagent according to the manufacturer’s in- The list of common DN genes was subjected to the Ma’ayan labo- structions (Invitrogen). The quality of RNA was confirmed with ratory’s Enrichr analysis (https://maayanlab.cloud/Enrichr). Specif- an Agilent 2100 Bioanalyzer. Clariom S human HT microarray ically, we used the following libraries: ENCODE_and_ChEA_ plates (Applied Biosystems) were processed at VHIR’s High Tech- Consensus_TFs_from_ChIP-X, Kinase_Perturbations_from_GEO_ nology Unit. Gene expression was analyzed with Partek Genomic down, Kinase_Perturbations_from_GEO_up, L1000_Kinase_and_ suite (Partek Incorporated) and R 4.1.0 (https://www.R-project GPCR_Perturbations_down, and L1000_Kinase_and_GPCR_ .org) under R Studio desktop version 1.4.1717. Raw expression Perturbations_up. values were obtained directly from .CEL files and preprocessed using the robust multiarray average method (Irizarry et al. 2003). Differentially expressed genes between groups were ob- tained after applying a linear model analysis with empirical Bayes Cbioportal for cancer genomics data moderation of the variance estimates by using the limma package For cancer genomics data, we accessed the skin cutaneous melano- from Bioconductor. To deal with multiple testing issues, P-values ma (TCGA and PanCancer Atlas) data set (https://www.cbioportal were adjusted to obtain strong control over the false discovery .org/study/summary?id=skcm_tcga_pan_can_atlas_2018). rate (FDR) using the Benjamini and Hochberg method (Benjamini and Hochberg 1995). The following R packages were used: Tidyverse (ggplot2, tidyr, Omomyc-modulated genes and patient survival relation study dplyr, and forcats), ggforce, ggrepel, pheatmap, survminer, ggvenn, ggridges, circlize, cowplot, and Bioconductor (oligo, clar- We analyzed melanoma patient survival according to the expres- iomshumantranscriptcluster.db, sva, and limma). sion of each of the common Omomyc-induced DEGs (UP + DN Gene set enrichment analysis (GSEA) was performed using pub- genes). Lower than the median expression of DN genes or higher licly available software provided by the Broad Institute (version 3.0) than the median expression of UP genes that associated with sig- with the hallmark, curated, motif, GO, and oncogenic signature nificantly longer OS or PFS were considered. We also included gene sets from the MSigDB (http://www.broadinstitute.org/gsea/ genes whose expression were in quartiles 1 (highest) or 4 (lowest) msigdb). The number of permutations was set to 1000, and the and associated with significantly longer OS or PFS. The full list of genes were ranked according to Signal2Noise (cell lines) or log2 ra- genes is in Supplemental Table S10. tio of classes (mouse tumors). Generation of gene signatures based on Omomyc-induced down- Gene categories based on melanoma- and MYC-related gene sets regulated (DN) and up-regulated (UP) genes Gene categories depicted in Figure 4 were manually curated based From the list of common DN and UP genes associated with im- on gene sets relevant to melanoma and MYC biology. These cat- proved survival, we tested which combination of three DN and egories consisted of up to 2000 genes. The complete list of gene three UP genes would discriminate patients with good or poor sets and their corresponding genes is in Supplemental Table S5. survival and represent a significant proportion of the patients in To link common down-regulated genes by Omomyc in vivo the study. The BIRC5–CCNB2–ORC1–DDX60–DTX3L–PARP9 with the genes in these categories, we generated a binary matrix signature discriminated 45 patients with good prognosis and 63 where 1 denotes connection and 0 indicates the absence of it. This patients with poor prognosis. The analysis in cBioPortal is avail- matrix then was used to create the chord diagram with the Circl- able at https://bit.ly/3xApUj7. ize package. Skin cutaneous melanoma (TCGA and PanCancer Atlas) patient String analysis information The lists of common DN and UP genes were subjected to unsu- Once we classified patients according to the gene signatures, we pervised string analysis at https://string-db.org. The settings looked at clinical attributes and mRNA under “comparison” to were as follows: network type: full STRING network; meaning look for patient characteristics that could explain or be related of network edges: confidence; active interaction sources: all; min- to their differences in survival. imum required interaction score: medium confidence (0.400); maximum number of interactions to show: first shell: none/query proteins only, second shell: none; and network clustering: no clustering. Preranked GSEA Subsequent supervised analysis was performed with the same Gene set enrichment analysis of patients with good or bad prog- settings except for the network clustering, where the number of nosis was performed as preranked. To do so, we downloaded clusters was set to three (DN genes) or two (UP genes) (string anal- mRNA expression data from patients belonging to both groups ysis of DN genes: https://version-11-5.string-db.org/cgi/network? and ordered the genes according to their fold change. GSEA networkId=bzTgvKhrb4qU; string analysis of UP genes: https v4.1.0 with the hallmark, curated, motif, GO, and oncogenic sig- ://version-11-5.string-db.org/cgi/network? nature gene sets from the MSigDB was used. Among the parame- networkId=bPIrLa5TIVBo). ters, we used “classic” enrichment statistic and “meandiv” The list of genes belonging to every cluster was downloaded normalization score. and subjected to the “investigate gene sets” tool from Molecular Signatures Database (MSigDB) (Subramanian et al. 2005), from which we obtained the lists of gene sets to which these genes be- Code availability longed. Finally, based on these lists, we assigned the gene catego- ries to which each cluster belonged. The full list of genes and gene All of the code for the bioinformatic analysis will be shared on sets is in Supplemental Table 8, A–F. request. GENES & DEVELOPMENT 317 Zacarías-Fluck et al. Data availability statement ysis and the investigation. S.C.-S. performed the formal analysis. T.J., S.M.-M., and M.-E.B. performed the methodology. G.M.-F., The data generated in this study are available here and in the Sup- E.S.d.P., L.F., and J.G. performed the investigation. L.N. curated plemental Data files. Microarray data are publicly available in the data and performed the formal analysis of the data. J.R.W. ac- Gene Expression Omnibus (GEO) as follows: A375 in vitro micro- quired the funding, performed the methodology, and reviewed array: GSE227840, A375 in vivo microarray: GSE227841, and edited the manuscript. L.S. conceived the study, acquired SkMel147 in vivo microarray: GSE227842, and SkMel147 lymph the funding, adminstered and supervised the project, and re- node metastases microarray: GSE227843. viewed and edited the manuscript. Statistical analysis All analyses, except the bioinformatic ones, were done using References GraphPad Prism 6 software. Normal distribution of the data Álamo MDC, Ochenduszko S, Crespo G, Corral M, Oramas J, was assessed for each group using D’Agostino–Pearson test. Dif- Sancho P, Medina J, Garicano F, Lopez P, Campos Balea B, ferences in samples’ mean values were analyzed using Student’s et al. 2021. 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Notch signaling in melanoma: interacting path- ways and stromal influences that enhance notch targeting. Competing interest statement Pigment Cell Melanoma Res 27: 162–168. doi:10.1111/pcmr .12194 M.F.Z.-F., S.C.-S., L.F., and J.R.W. are shareholders of Peptomyc, a Benjamini Y, Hochberg Y. 1995. Controlling the false discovery company focused on developing Myc inhibitors for cancer treat- rate: a practical and powerful approach to multiple testing. J ment. D.M.-V., S.C.-S., T.J., S.M.-M., G.M.-F., L.F., J.G., and R Stat Soc B 57: 289–300. doi:10.1111/j.2517-6161.1995 M.-E.B. report personal fees from Peptomyc during the comple- .tb02031.x tion of this project. L.S. and M.B. are cofounders and shareholders Bennett DC. 2016. Genetics of melanoma progression: the rise of Peptomyc. and fall of cell senescence. Pigment Cell Melanoma Res 29: 122–140. doi:10.1111/pcmr.12422 Biroccio A, Benassi B, Amodei S, Gabellini C, Del Bufalo D, Zupi Acknowledgments G. 2001. c-Myc down-regulation increases susceptibility to cisplatin through reactive oxygen species-mediated apoptosis M.F.Z.-F. was supported by the Juan de la Cierva Programme of in M14 human melanoma cells. Mol Pharmacol 60: 174–182. the Spanish Ministry of Economy and Competitiveness (IJCI- 2014-22403) and Fundació La Marató de TV3 (grant 474/C/ doi:10.1124/mol.60.1.174 2019); F.G. was supported by Spanish Ministry of Science and In- The Cancer Genome Atlas Network. 2015. Genomic classifica- novation Contratos Predoctorales de Formación en Investigación tion of cutaneous melanoma. Cell 161: 1681–1696. doi:10 en Salud (PFIS; FI20/00274); I.G.-L. was supported by a grant from .1016/j.cell.2015.05.044 the University Teacher Training Program (FPU), Ministry of Uni- Chen EY, Tan CM, Kou Y, Duan Q, Wang Z, Meirelles GV, Clark versities (FPU20/04812); and S.M.-M. was supported by the Gen- NR, Ma’ayan A. 2013. Enrichr: interactive and collaborative eralitat de Catalunya “Contractació de Personal Investigador HTML5 gene list enrichment analysis tool. BMC Bioinfor- Novell (FI-DGR)” 2016 fellowship (2016FI_B 00592). This project matics 14: 128. doi:10.1186/1471-2105-14-128 was funded by grants from the Spanish Ministry of Science and In- Chen S, Liu Z, Li M, Huang Y, Wang M, Zeng W, Wei W, Zhang C, novation (Fondo de Inversión en Salud [FIS] PI19/01277, which Gong Y, Guo L. 2020. 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