Oncogenic alterations to DNA are not transforming in all cellular contexts1,2. This may be due to pre-existing transcriptional programmes in the cell of origin. Here we define anatomic position as a major determinant of why cells respond to specific oncogenes. Cutaneous melanoma arises throughout the body, whereas the acral subtype arises on the palms of the hands, soles of the feet or under the nails3. We sequenced the DNA of cutaneous and acral melanomas from a large cohort of human patients and found a specific enrichment for BRAF mutations in cutaneous melanoma and enrichment for CRKL amplifications in acral melanoma. We modelled these changes in transgenic zebrafish models and found that CRKL-driven tumours formed predominantly in the fins of the fish. The fins are the evolutionary precursors to tetrapod limbs, indicating that melanocytes in these acral locations may be uniquely susceptible to CRKL. RNA profiling of these fin and limb melanocytes, when compared with body melanocytes, revealed a positional identity gene programme typified by posterior HOX13 genes. This positional gene programme synergized with CRKL to amplify insulin-like growth factor (IGF) signalling and drive tumours at acral sites. Abrogation of this CRKL-driven programme eliminated the anatomic specificity of acral melanoma. These data suggest that the anatomic position of the cell of origin endows it with a unique transcriptional state that makes it susceptible to only certain oncogenic insults.
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All plasmids generated in this study will be either deposited with Addgene or available upon request. All fish lines will be made available through Zebrafish International Resource Center (ZIRC) or by request. All cell lines are available by request. Human RNA-seq data that support the findings of this study have been deposited in the European Genome-phenome Archive (EGA) and are available under study accession EGAS00001001552 with dataset accession EGAD00001006439. Patient sample information and differential expression tables are provided in Supplementary Table 5. The zebrafish fin versus body bulk RNA-seq experiment is available via the NCBI Gene Expression Omnibus (GEO) repository under identifier code GSE158538, with bulk RNA-seq counts and differential expression tables in Supplementary Table 4. The zebrafish fin versus body single cell RNA-seq experiment is available via the NCBI GEO repository under the identifier code GSE181748. Original source data for ChIP–seq analysis is from ref. 31 and can be found at GEO GSE81358. The pathway analysis is made available in Supplementary Table 6. HOXA13 Cut & Run data are available via the NCBI GEO repository under identifier code GSE181768. MACS2 peak scores and pathway analysis are available in Supplementary Table 7. CRKL immunoprecipitation–mass spectrometry data are available at ProteomeXchange with identifier PXD027968 and in Supplementary Table 8.
The Matlab script for tailfin image analysis and R scripts for RNA-seq, ChIP–seq pathway analysis, Cut&Run and immunoprecipitation–mass spectrometry analysis is available on Github (https://github.com/jmweiss18/Weiss_Nature_2022).
Tang, J. et al. The genomic landscapes of individual melanocytes from human skin. Nature 586, 600–605 (2020).
Fowler, J. C. et al. Selection of oncogenic mutant clones in normal human skin varies with body site. Cancer Discov. 11, 340–361 (2020).
Reed, R. In New Concepts in Surgical Pathology of the Skin 89–90 (Wiley, 1976).
Wang, K. C., Helms, J. A. & Chang, H. Y. Regeneration, repair and remembering identity: the three Rs of Hox gene expression. Trends Cell Biol. 19, 268–275 (2009).
Curtin, J. A. et al. Distinct sets of genetic alterations in melanoma. N. Engl. J. Med. 353, 2135–2147 (2005).
Hayward, N. K. et al. Whole-genome landscapes of major melanoma subtypes. Nature 545, 175–180 (2017).
Petrelli, F. et al. Prognostic survival associated with left-sided vs right-sided colon cancer: a systematic review and meta-analysis. JAMA Oncol. 3, 211–219 (2017).
Rabbie, R., Ferguson, P., Molina-Aguilar, C., Adams, D. J. & Robles-Espinoza, C. D. Melanoma subtypes: genomic profiles, prognostic molecular markers and therapeutic possibilities. J. Pathol. 247, 539–551 (2019).
Belote, R. L. et al. Human melanocyte development and melanoma dedifferentiation at single-cell resolution. Nat. Cell Biol. 23, 1035–1047 (2021).
Moon, H. et al. Melanocyte stem cell activation and translocation initiate cutaneous melanoma in response to UV exposure. Cell Stem Cell 21, 665–678.e666 (2017).
Kohler, C. et al. Mouse cutaneous melanoma induced by mutant Braf arises from expansion and dedifferentiation of mature pigmented melanocytes. Cell Stem Cell 21, 679–693.e676 (2017).
Newell, F. et al. Whole-genome sequencing of acral melanoma reveals genomic complexity and diversity. Nat. Commun. 11, 5259 (2020).
Yeh, I. et al. Targeted genomic profiling of acral melanoma. J. Natl Cancer Inst. 111, 1068–1077 (2019).
Liang, W. S. et al. Integrated genomic analyses reveal frequent TERT aberrations in acral melanoma. Genome Res. 27, 524–532 (2017).
Klemen, N. D. et al. Survival after checkpoint inhibitors for metastatic acral, mucosal and uveal melanoma. J. Immunother. Cancer 8, e000341 (2020).
Shoushtari, A. N. et al. The efficacy of anti-PD-1 agents in acral and mucosal melanoma. Cancer 122, 3354–3362 (2016).
Cerami, E. et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2, 401–404 (2012).
Zehir, A. et al. Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients. Nat. Med. 23, 703–713 (2017).
Luo, L. Y. & Hahn, W. C. Oncogenic signaling adaptor proteins. J. Genet. Genomics 42, 521–529 (2015).
Bentires-Alj, M. et al. A role for the scaffolding adapter GAB2 in breast cancer. Nat. Med. 12, 114–121 (2006).
Cheung, H. W. et al. Amplification of CRKL induces transformation and epidermal growth factor receptor inhibitor resistance in human non-small cell lung cancers. Cancer Discov. 1, 608–625 (2011).
Hemmeryckx, B. et al. Crkl enhances leukemogenesis in BCR/ABL P190 transgenic mice. Cancer Res. 61, 1398–1405 (2001).
Chernoff, K. A. et al. GAB2 amplifications refine molecular classification of melanoma. Clin. Cancer Res. 15, 4288–4291 (2009).
Horst, B. et al. Gab2-mediated signaling promotes melanoma metastasis. Am. J. Pathol. 174, 1524–1533 (2009).
Eshiba, S. et al. Stem cell spreading dynamics intrinsically differentiate acral melanomas from nevi. Cell Rep. 36, 109492 (2021).
Nakamura, T., Gehrke, A. R., Lemberg, J., Szymaszek, J. & Shubin, N. H. Digits and fin rays share common developmental histories. Nature 537, 225–228 (2016).
Shubin, N. H., Daeschler, E. B. & Jenkins, F. A. Jr The pectoral fin of Tiktaalik roseae and the origin of the tetrapod limb. Nature 440, 764–771 (2006).
Xu, B., Feng, X. & Burdine, R. D. Categorical data analysis in experimental biology. Dev. Biol. 348, 3–11 (2010).
Philippidou, P. & Dasen, J. S. Hox genes: choreographers in neural development, architects of circuit organization. Neuron 80, 12–34 (2013).
Petit, F., Sears, K. E. & Ahituv, N. Limb development: a paradigm of gene regulation. Nat. Rev. Genet. 18, 245–258 (2017).
Sheth, R. et al. Distal limb patterning requires modulation of cis-regulatory activities by HOX13. Cell Rep. 17, 2913–2926 (2016).
Li, S. et al. Cistrome-GO: a web server for functional enrichment analysis of transcription factor ChIP–seq peaks. Nucleic Acids Res. 47, W206–W211 (2019).
Chablais, F. & Jazwinska, A. IGF signaling between blastema and wound epidermis is required for fin regeneration. Development 137, 871–879 (2010).
Dhupkar, P., Zhao, H., Mujoo, K., An, Z. & Zhang, N. Crk II silencing down-regulates IGF-IR and inhibits migration and invasion of prostate cancer cells. Biochem. Biophys. Rep. 8, 382–388 (2016).
Zhang, J. et al. CRKL mediates p110β-dependent PI3K signaling in PTEN-deficient cancer cells. Cell Rep. 20, 549–557 (2017).
Tanna, C. E., Goss, L. B., Ludwig, C. G. & Chen, P. W. Arf GAPs as regulators of the actin cytoskeleton—an update. Int. J. Mol. Sci. 20, 442 (2019).
Fritsch, R. et al. RAS and RHO families of GTPases directly regulate distinct phosphoinositide 3-kinase isoforms. Cell 153, 1050–1063 (2013).
Ye, L., Robertson, M. A., Mastracci, T. L. & Anderson, R. M. An insulin signaling feedback loop regulates pancreas progenitor cell differentiation during islet development and regeneration. Dev. Biol. 409, 354–369 (2016).
Zhang, Y. M. et al. Distant insulin signaling regulates vertebrate pigmentation through the sheddase Bace2. Dev. Cell 45, 580–594.e587 (2018).
Baggiolini, A. et al. Developmental chromatin programs determine oncogenic competence in melanoma. Science 373, eabc1048 (2021).
Farshidfar, F. et al. Integrative molecular and clinical profiling of acral melanoma links focal amplification of 22q11.21 to metastasis. Nat Commun 13, 898 (2022). https://doi.org/10.1038/s41467-022-28566-4
Kim, K. et al. Clinicopathologic characteristics of early gastric cancer according to specific intragastric location. BMC Gastroenterol. 19, 24 (2019).
Razumilava, N. & Gores, G. J. Cholangiocarcinoma. Lancet 383, 2168–2179 (2014).
Tang, Q. et al. Anatomic mapping of molecular subtypes in diffuse glioma. BMC Neurol. 17, 183 (2017).
White, R. M. et al. Transparent adult zebrafish as a tool for in vivo transplantation analysis. Cell Stem Cell 2, 183–189 (2008).
White, R. M. et al. DHODH modulates transcriptional elongation in the neural crest and melanoma. Nature 471, 518–522 (2011).
Kaufman, C. K. et al. A zebrafish melanoma model reveals emergence of neural crest identity during melanoma initiation. Science 351, aad2197 (2016).
The Cancer Genome Atlas Research Network. Genomic classification of cutaneous melanoma. Cell 161, 1681–1696 (2015).
Dankort, D. et al. BrafV600E cooperates with Pten loss to induce metastatic melanoma. Nat. Genet. 41, 544–552 (2009).
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).
Li, H. & Durbin, R. Fast and accurate long-read alignment with Burrows–Wheeler transform. Bioinformatics 26, 589–595 (2010).
Cibulskis, K. et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat. Biotechnol. 31, 213–219 (2013).
Shen, R. & Seshan, V. E. FACETS: allele-specific copy number and clonal heterogeneity analysis tool for high-throughput DNA sequencing. Nucleic Acids Res. 44, e131 (2016).
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
Li, B. & Dewey, C. N. RSEM- accurate transcript quantification from RNA-seq data with or without a reference genome. BMC. Bioinformatics 12, 1471–2105 (2011).
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
Korotkevich, G., Sukhov, V. & Sergushichev, A. Fast gene set enrichment analysis. Preprint at https://doi.org/10.1101/060012 (2019).
Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).
Khan, A. et al. JASPAR 2018: update of the open-access database of transcription factor binding profiles and its web framework. Nucleic Acids Res. 46, D260–D266 (2018).
Grossman, R. L. et al. Toward a shared vision for cancer genomic data. N. Engl. J. Med. 375, 1109–1112 (2016).
Hoadley, K. A. et al. Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin. Cell 158, 929–944 (2014).
The Cancer Genome Atlas Research Network. Comprehensive and integrated genomic characterization of adult soft tissue sarcomas. Cell 171, 950–965.e928 (2017).
The Cancer Genome Atlas Research Network. Comprehensive and integrative genomic characterization of hepatocellular carcinoma. Cell 169, 1327–1341.e1323 (2017).
Robertson, A. G. et al. Comprehensive molecular characterization of muscle-invasive bladder cancer. Cell 171, 540–556.e525 (2017).
Fishbein, L. et al. Comprehensive molecular characterization of pheochromocytoma and paraganglioma. Cancer Cell 31, 181–193 (2017).
The Cancer Genome Atlas Research Network. Comprehensive molecular profiling of lung adenocarcinoma. Nature 511, 543–550 (2014).
The Cancer Genome Atlas Research Network. Comprehensive molecular characterization of gastric adenocarcinoma. Nature 513, 202–209 (2014).
The Cancer Genome Atlas Research Network. Comprehensive molecular characterization of urothelial bladder carcinoma. Nature 507, 315–322 (2014).
The Cancer Genome Atlas Research Network. Comprehensive molecular characterization of human colon and rectal cancer. Nature 487, 330–337 (2012).
The Cancer Genome Atlas Research Network. Comprehensive molecular portraits of human breast tumours. Nature 490, 61–70 (2012).
The Cancer Genome Atlas Research Network. Comprehensive genomic characterization of squamous cell lung cancers. Nature 489, 519–525 (2012).
Ciriello, G. et al. Comprehensive molecular portraits of invasive lobular breast cancer. Cell 163, 506–519 (2015).
The Cancer Genome Atlas Research Network. The molecular taxonomy of primary prostate cancer. Cell 163, 1011–1025 (2015).
The Cancer Genome Atlas Research Network. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 455, 1061–1068 (2008).
The Cancer Genome Atlas Research Network. Comprehensive genomic characterization of head and neck squamous cell carcinomas. Nature 517, 576–582 (2015).
The Cancer Genome Atlas Research Network. Integrated genomic characterization of endometrial carcinoma. Nature 497, 67–73 (2013).
The Cancer Genome Atlas Research Network. Comprehensive molecular characterization of clear cell renal cell carcinoma. Nature 499, 43–49 (2013).
Davis, C. F. et al. The somatic genomic landscape of chromophobe renal cell carcinoma. Cancer Cell 26, 319–330 (2014).
The Cancer Genome Atlas Research Network. Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. N. Engl. J. Med. 368, 2059–2074 (2013).
The Cancer Genome Atlas Research Network. Integrated genomic analyses of ovarian carcinoma. Nature 474, 609–615 (2011).
Brennan, C. W. et al. The somatic genomic landscape of glioblastoma. Cell 155, 462–477 (2013).
The Cancer Genome Atlas Research Network. Integrated genomic characterization of papillary thyroid carcinoma. Cell 159, 676–690 (2014).
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).
Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
Ramirez, F. et al. deepTools2: a next generation web server for deep-sequencing data analysis. Nucleic Acids Res. 44, W160–W165 (2016).
Zhang, Y. et al. Model-based analysis of ChIP–seq (MACS). Genome Biol. 9, R137 (2008).
Skene, P. J., Henikoff, J. G. & Henikoff, S. Targeted in situ genome-wide profiling with high efficiency for low cell numbers. Nat. Protoc. 13, 1006–1019 (2018).
Kall, L., Canterbury, J. D., Weston, J., Noble, W. S. & MacCoss, M. J. Semi-supervised learning for peptide identification from shotgun proteomics datasets. Nat. Methods 4, 923–925 (2007).
The, M., MacCoss, M. J., Noble, W. S. & Kall, L. Fast and accurate protein false discovery rates on large-scale proteomics data sets with Percolator 3.0. J. Am. Soc. Mass. Spectrom. 27, 1719–1727 (2016).
Sparks, A. B. et al. Distinct ligand preferences of Src homology 3 domains from Src, Yes, Abl, Cortactin, p53bp2, PLCy, Crk, and Grb2. Proc. Natl Acad. Sci. USA 93, 1540–1544 (1996).
Birge, R. B., Kalodimos, C., Inagaki, F. & Tanaka, S. Crk and CrkL adaptor proteins: networks for physiological and pathological signaling. Cell Commun. Signal. 7, 13 (2009).
Tothova, Z. et al. Multiplex CRISPR/Cas9-based genome editing in human hematopoietic stem cells models clonal hematopoiesis and myeloid neoplasia. Cell Stem Cell 21, 547–555.e548 (2017).
Lindsay, H. et al. CrispRVariants charts the mutation spectrum of genome engineering experiments. Nat. Biotechnol. 34, 701–702 (2016).
DeLuca, D. S. et al. RNA-SeQC: RNA-seq metrics for quality control and process optimization. Bioinformatics 28, 1530–1532 (2012).
Hu, Y. et al. An integrative approach to ortholog prediction for disease-focused and other functional studies. BMC Bioinf. 12, 1471–2105 (2011).
Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587.e3529 (2021).
Hafemeister, C. & Satija, R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol. 20, 296 (2019).
Jolliffe, I. T. Principal Component Analysis and Factor Analysis (Springer, 1986).
McInnes, L., Healy, J. & Melville, J. UMAP: uniform manifold approximation and projection for dimension reduction. Preprint at https://doi.org/10.48550/arXiv.1802.03426 (2018).
Baron, M. et al. The stress-like cancer cell state is a consistent component of tumorigenesis. Cell Syst. 11, 536–546.e537 (2020).
Hunter, M. V., Moncada, R., Weiss, J. M., Yanai, I. & White, R. M. Spatially resolved transcriptomics reveals the architecture of the tumor-microenvironment interface. Nat. Commun. 12, 6278 (2021).
Freese, N. H., Norris, D. C. & Loraine, A. E. Integrated genome browser: visual analytics platform for genomics. Bioinformatics 32, 2089–2095 (2016).
J.M.W. was supported by the Kirschstein-NRSA predoctoral fellowship (F30) from the NIH under award number F30CA236442 and by a predoctoral fellowship (T32) by the Cell and Developmental Biology Program at Weill Cornell Graduate School supported by the NIH under award number T32GM008539. N.R.C. was supported by the Kirschstein-NRSA predoctoral fellowship (F30) from the NIH under award number F30CA220954. J.M.W., Y.M. and N.R.C were supported by a Medical Scientist Training Program grant from the NIH under award number T32GM007739. A.B. was supported by the Swiss National Science Foundation Postdoc mobility fellowships P2ZHP3_171967 and P400PB_180672. M.T. was supported by the Marie-Josée Kravis Women in Science Endeavor Postdoctoral Fellowship. E.D.M. was supported by the Predoctoral to Postdoctoral Fellow Transition Award (F99/K00) from the NIH under award number K00CA223016. R.A.S., G.V.L., J.F.T., G.J.M. and N.K.H. are supported by an Australian National Health and Medical Research Council Program Grant. R.A.S., N.K.H. and G.V.L. are supported by the Australian National Health and Medical Research Council Fellowship programme. The authors acknowledge support from colleagues at their respective institutions and support from the Cameron Family and the Ainsworth Foundation. This research was funded in part through the NIH/NCI Cancer Center Support Grant P30 CA008748; Ludwig Collaborative and Swim Across America Laboratory, Memorial Sloan Kettering Cancer Center; Parker Institute for Cancer Immunotherapy, Memorial Sloan Kettering Cancer Center; Department of Medicine, Memorial Sloan Kettering Cancer Center; and Weill Cornell Medicine. This work was supported by awards from the Melanoma Research Alliance, The Debra and Leon Black Family Foundation, NIH Research Program Grants R01CA229215 and R01CA238317, NIH Director’s New Innovator Award DP2CA186572, The Pershing Square Sohn Foundation, The Mark Foundation for Cancer Research, The American Cancer Society, The Alan and Sandra Gerry Metastasis Research Initiative at the Memorial Sloan Kettering Cancer Center, The Harry J. Lloyd Foundation, Consano and the Starr Cancer Consortium (all to R.M.W.). We thank L. T. Koufariotis for technical assistance with the alignment of human RNA-seq data. We acknowledge the use of the Integrated Genomics Operation Core, funded by the NCI Cancer Center Support Grant (CCSG, P30 CA08748), Cycle for Survival, and the Marie-Josée and Henry R. Kravis Center for Molecular Oncology.
S.M. consulted for Boehringer-Ingelheim. J.F.T. has received honoraria for advisory board participation from BMS Australia, MSD Australia, GSK and Provectus, and travel support from GSK and Provectus. R.A.S. has received fees for professional services from Qbiotics, Novartis, MSD Sharp & Dohme, NeraCare, AMGEN, Bristol-Myers Squibb, Myriad Genetics and GlaxoSmithKline. G.V.L. is consultant advisor for Aduro Biotech, Amgen, Array Biopharma, Boehringer Ingelheim International GmbH, Bristol-Myers Squibb, Evaxion Biotech A/S, Hexel AG, Highlight Therapeutics S.L., Merck Sharpe & Dohme, Novartis Pharma AG, OncoSec, Pierre Fabre, QBiotics Group Limited, Regeneron Pharmaceuticals, SkylineDX B.V. and Specialised Therapeutics Australia. J.V.P. and N.W. are equity holders and board members of genomiQa. P.J. is currently employed by Celsius Therapeutics. B.S.T. reports receiving Honoria and research funding from Genentech and Illumina and advisory board activities for Boehringer Ingelheim and Loxo Oncology, a wholly owned subsidiary of Eli Lilly. B.S.T. is currently employed by Loxo Oncology. L.S. is co-founder and consultant of BlueRock Therapeutics. D.B.S. has consulted with/received honoraria from Pfizer, Loxo Oncology, Lilly Oncology, Vivideon Therapeutics, Q.E.D. Therapeutics and Illumina. J.D.W. is a consultant for Amgen, Apricity, Arsenal, Ascentage Pharma, Astellas, Boehringer Ingelheim, Bristol Myers Squibb, Eli Lilly, F Star, Georgiamune, Imvaq, Kyowa Hakko Kirin, Merck, Neon Therapeutics, Polynoma, Psioxus, Recepta, Trieza, Truvax and Sellas. J.D.W. has grant and research support from Bristol Meyers Squibb and Sephora. J.D.W. has equity in Tizona Pharmaceuticals, Imvaq, Beigene, Linneaus, Apricity, Arsenal IO and Georgiamune. T.M. is a consultant for Leap Therapeutics, Immunos Therapeutics and Pfizer, and co-founder of Imvaq therapeutics. T.M. has equity in Imvaq therapeutics. T.M. reports grants from Bristol Myers Squibb, Surface Oncology, Kyn Therapeutics, Infinity Pharmaceuticals, Peregrine Pharmaceuticals, Adaptive Biotechnologies, Leap Therapeutics and Aprea. T.M. is inventor on patent applications related to work on oncolytic viral therapy, alphavirus-based vaccines, neo-antigen modelling, CD40, GITR, OX40, PD-1 and CTLA-4. N.R. is on the scientific advisory board and receives research funding from Chugai, is on the scientific advisory board and owns equity in Beigene and Fortress. N.R. is also on the scientific advisory board of Daiichi-Sankyo, Astra-Zeneca-MedImmune and F-Prime, and is a past scientific advisory board member of Millenium-Takeda, Kadmon, Kura and Araxes. N.R. is a consultant to Novartis, Boehringer Ingelheim, Tarveda and Foresight and consulted in the last three years with Eli Lilly, Merrimack, Kura Oncology, Araxes and Kadman. N.R. owns equity in ZaiLab, Kura Oncology, Araxes and Kadman. N.R. also collaborates with Plexxikon. R.M.W. is a paid consultant to N-of-One Therapeutics, a subsidiary of Qiagen. R.M.W. receives royalty payments for the use of the casper line from Carolina Biologicals.
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Extended data figures and tables
Related to Fig. 1. (a− b) MSK-IMPACT of biologically independent samples from n = 100 acral and n = 839 cutaneous melanoma patients. Two-sided Fisher’s exact test was used to compare the frequency of the most recurrently mutated and deleted genes by melanoma subtype. Both coding and promoter mutations were counted for TERT. (c) RNA-seq of biologically independent samples from n = 61 acral and n = 53 cutaneous melanoma patient. Boxplots compare indicated genes by subtype. Box minima = 25th percentile, centre = 50th percentile, maxima = 75th percentile. Whiskers extends from the box maxima/minima to the largest/smallest value no further than 1.5 * IQR (interquartile range) from the box maxima/minima. Data beyond the end of the whiskers are plotted individually as outliers. FDR = 0.05 and adjusted p-values were calculated using a two-sided Wald test with DESeq2. (d) Schematic detailing predicted synergistic interaction between putative driver genes in acral melanoma. (e) TCGA pan-cancer analysis shows significant co-occurrence of CRKL, GAB2, NF1, and TERT alterations. Data analyzed with one-sided Fisher’s exact test and p-values adjusted for FDR = 0.05. (f) Clinical course of an acral melanoma patient. (g) WGS copy number profile showing copy number changes in GAB2, CRKL, and NF1. (h) Putative drivers of patient’s acral melanoma. * p-value < 0.05, ** p-value < 0.01, *** p-value < 0.001, **** p-value < 0.0001.
Related to Fig. 1. (a) Plasmids used to create the WT melanocyte model, acral melanoma model, and cutaneous melanoma models. (b) Genotyping PCR to confirm integration of plasmids into zebrafish genome. Representative images of n = 3 independent replicates. For gel source data, see Supplementary Fig. 3. (c) qPCR to validate RNA expression of transgenes. N = 3 biological independent replicates. Error bars = SEM. (d) Western blot was performed on WT and acral melanoma fish for human CRKL and GAB2 to validate transgene expression. N = 3 biological replicates are shown. For western blot source data, see Supplementary Fig. 4. (e) RNA-seq on embryos at 5 days post-fertilization FACS sorted for GFP+ melanocytes. Log normalized expression of melanocyte markers and acral transgenes are indicated. N = 4 biologically independent replicates. P-values calculated using a two-sided Wald test with DESeq2. Error bars = SEM. (f) CRISPR-seq was performed on the predicted sgRNA cut locus of zebrafish nf1a and nf1b to sensitively detect Cas9-mediated editing. Reference genome as well as the two most commonly altered reads are shown. The right panel is a heatmap with the frequency of reference and edited sequences in WT melanocyte and acral melanoma models. Variants displayed for both nf1a and nf1b are frameshift mutations in exon 1 leading to a predicted loss of function.(g) Immunofluorescence on transverse sections of WT melanocyte model, tumor-bearing acral melanoma model, and tumor-bearing cutaneous melanoma model for GFP and SOX10. GFP labels all tumor cells and SOX10 is used as a melanocyte lineage marker. Asterisks indicate blood vessels with autofluorescence. Representative of n = 3 biological replicates. (h) Histogram representation of the ternary plot portrayed in Fig. 1e showing the percentage of tumors forming in the head, body, or fins of acral fish and cutaneous fish melanoma models. Data represents n = 3 biological replicates, which range from n = 43 to n = 141 fish per replicate. See Supplementary Table 3 showing the exact number of fish and corresponding percentages for each replicate. To compare overall anatomic distribution between the two genotypes, a chi-squared test was performed. To compare the frequency of tumors at each anatomic location, a student’s two-sided t-test was performed. Error bars = SEM. Ternary plot represents the same data presented in the histogram. (i) WT melanocyte vs acral melanoma model compared for melanocyte area in tailfin at 3-days post-fertilization. Data represents n = 29 WT fish and n = 41 acral fish pooled from n = 4 biological replicates. Each point represents the tailfin melanocyte area of a different animal. P-values generated by a two-sided student’s t-test. Error bars = SEM. * p-value < 0.05, ** p-value < 0.01, *** p-value < 0.001, **** p-value < 0.0001.
Related to Fig. 1. n = 3 WT, acral, and cutaneous melanoma transgenic fish were used for histological profiling. Images of the fish used for histology are shown on top with a red line indicating anatomic region used for sectioning and profiling. For WT fish, head, body, and fin regions were used as a negative control. For acral fish, profiled tumors were located on the fins. For cutaneous fish, profiled tumors were located on the body and head. The markers used include H&E, GFP (marking melanocytes in all 3 models), CRKL (expressed only in the acral model), pIGF1R, pERK, and pS6. See methods for details regarding antibody, concentration, and staining procedure.
Related to Fig. 1. (a) F0 transgenic fish shown in Fig. 1d and Fig. 2b were outcrossed to generate stable germline transgenics. Representative images of each transgenic line are shown with arrows to indicate the location of tumors. (b) Tumor frequency of each stable line at 1 year post fertilization. (c) Ternary plot showing the anatomic distribution of tumors for each stable transgenic line. P-values generated by chi-squared test. See Supplementary Table 3 for a full list of fish and tumor numbers across all replicates and experimental conditions. (d) Adult WT melanocyte and acral melanoma models were dissected to isolate body skin and fins and then analyzed via flow cytometry. The images show representative differences in pigment patterning between the two models. The histogram shows the percentage of total cells that were GFP+ grouped by anatomic location and genotype. Data represents n = 3 biological replicates. Each replicate represents the pooling of n = 2 male and n = 2 female fish. P-values generated by student’s two-sided t-test. Error bars = SEM. (e) The ratio of fin melanocytes to body melanocytes was compared between the two models using the data from (d). Data represents n = 3 biological replicates. P-values generated by student’s two-sided t-test. Error bars = SEM. (f) Gating strategy of the flow cytometry data. (g) Representative contour plots of melanocyte frequency by genotype and location. * p-value < 0.05, ** p-value < 0.01, *** p-value < 0.001, **** p-value < 0.0001.
Related to Fig. 3. (a–c) Validation of successful isolation of melanocytes for RNA-seq. (a) Volcano plots comparing melanocytes (GFP+ sample) to their microenvironment (GFP- sample) across locations and transgenic models. Melanocyte markers are labeled. Genes with FDR-adjusted p-value < 0.05 indicated in blue. P-values calculated using DESeq2. (b) GSEA showing the list of the top pathways enriched in melanocytes (GFP+ sample) compared to their microenvironment (GFP- sample). Colors indicate p-value adjusted for FDR = 0.05. (c) Log normalized counts for the expression of transgenes across all samples. EGFP and mitfa expression are high in all melanocyte samples and CRKL, GAB2, TERT, and Cas9-mCherry expression is high only in acral melanoma model melanocytes. N = 3 biologically independent replicates. Box minima = 25th percentile, centre = 50th percentile, maxima = 75th percentile. Whiskers extends to the largest/smallest value no further than 1.5 * IQR (interquartile range) from the box maxima/minima. Data beyond the end of the whiskers are plotted individually as outliers. (d) Principal component analysis (PCA) for all samples showing principal components 1 (PC1) and 2 (PC2). The percent of transcriptional variation captured by each principal component is indicated. (e) GSEA pathway analysis comparing fin vs body melanocytes from the WT melanocyte model listing the top enriched pathways in fin melanocytes. Limb development and positional identity-related pathways are highlighted. See Supplementary Table 4 for a full list of pathways. (f) GSEA barcode plot showing enrichment of genes in the GO: Appendage Development pathway, generated with weighted kolmogorov smirnov (WKS) testing. NES and FDR = 0.05 adjusted p-values are indicated. (g− h) Volcano plots comparing fin melanocytes vs body melanocytes from the (g) WT melanocyte model and (h) acral melanoma model. Genes with FDR-adjusted p-value < 0.05 indicated in blue. P-values calculated using DESeq2.
Related to Fig. 3. (a) Schematic illustrating the fin versus body scRNA-sequencing experiment. Similar to the bulk RNA-seq experiment in Fig. 3a, body skin and fins were isolated from acral melanoma fish by dissection and then FACS sorted for GFP+ melanocytes and GFP- microenvironmental cells. This generated n = 4 samples (body melanocytes, body TME, fin melanocytes, fin TME) that each underwent scRNA-seq. (b) UMAP pooled for all 4 samples highlighting the various cell types captured by scRNA-seq. (c) Violin plot comparing hoxc13b expression in body and fin melanocytes. Data represents log normalized counts and was compared by two-sided Wilcoxon rank sum test. (d) Violin plot comparing total expression of all HOX13 genes (hoxa13a, hoxa13b, hoxb13a, hoxc13a, hoxc13b, hoxd13a). To avoid single-cell drop out of more lowly expressed HOX13 genes, log normalized counts were summed together to calculate total expression per cell. Data was analyzed by two-sided Wilcoxon rank sum test. (e) Violin plot comparing the total expression of all HOX13 genes across all fin and body cell types. Data was analyzed by two-sided Wilcoxon rank sum test. (f) Zebrafish hox genes detected by bulk RNA-seq were used to perform unsupervised clustering and visualized with a heatmap. Samples clustered by anatomic location, then lineage, and then genotype in that order, indicating the hox genes predominantly associate with anatomic position. Particular hox genes that are differentially expressed across all body and fin samples are indicated with a black box. * p-value < 0.05, ** p-value < 0.01, *** p-value < 0.001, **** p-value < 0.0001.
Related to Fig. 4. (a) GSEA pathway analysis for top enriched pathways in human acral melanoma and is controlled for disease stage. Limb development pathways and positional identity-related pathways are highlighted. See Supplementary Table 5 for a full list of pathways. (b) GSEA barcode plot showing enrichment for the GO: Appendage Development pathway in acral melanoma, generated with WKS testing. NES and FDR = 0.05 adjusted p-values are indicated. (c) PCA of all samples based on the expression of just HOX genes. Color indicates the combination of melanoma subtype and disease stage. (d) N = 3 acral melanoma tissue microarrays of n = 32 samples total from Sloan Kettering was used for staining for H&E, CRKL, HOXB13, and pIGF1R. (e) Extent and intensity of each sample was independently scored by a Sloan Kettering dermatopathologist and is represented in a pie chart. 2 samples were removed from the analysis due to heavy pigmentation, resulting in n = 30 samples total used for IHC quantification analysis. Extent scores range 0− 4 and intensity scores range 0-3. (f) N = 3 cutaneous melanoma tissue microarrays of n = 14 total samples from Sloan Kettering was used for staining for H&E, CRKL, HOXB13, and pIGF1R. (g) Extent and intensity of each sample was independently scored by a Sloan Kettering dermatopathologist and is represented in a pie chart. Extent scores range 0–4 and intensity scores range 0–3. (h) CRKL, HOXB13, and pIGF1R antibodies were optimized on human tissues chosen for their expression characterized by the Human Protein Atlas. CRKL is a ubiquitously expressed protein with relatively high expression in the colonic epithelia and lower expression in liver. HOXB13 is expressed in prostate epithelia with low expression in liver and skin (anatomic source unknown). pIGF1R demonstrates higher levels in prostate epithelia, colon epithelia, tonsil, and lower levels in skin. See methods for details regarding antibody, concentration, and staining procedure.
Related to Fig. 5. (a− e) Analysis of HOXA13, HOXD13, H3K27ac ChIP-seq data analyzed from Sheth et al., 201631 performed on developing limb buds from E11.5 mouse embryos. (a) Waterfall plot representation of 5594 top enriched GSEA pathways regulated by HOXA13, highlighting limb and insulin/IGF-related pathways. Limb development pathways were identified using the search terms “limb” or “appendage.” Insulin/IGF signaling pathways were identified using the search terms “insulin” or “IGF”. Only pathways with FDR < 0.05 are highlighted. (b− c) Histogram showing the significantly enriched insulin/IGF pathways regulated by HOXA13 and HOXD13. NES and p-value are indicated. (d− e) Integrated genome browser tracks for HOXA13 and HOXD13 ChIP-seq binding and H3K27 acetylation near the transcription start site of IGF1 and IGF2. A full list of pathways can be found in Supplementary Table 6. (f) GSEA pathway analysis for the top pathways enriched in fin melanocytes versus body melanocytes from the zebrafish acral melanoma model. IGF-related pathways are indicated in red. A full list of pathways can be found in Supplementary Table 4. (g) GSEA barcode plot comparing acral model fin vs body melanocytes showing enrichment of genes in the GO: Regulation of Multicellular Organisms Growth pathway, generated weighted WKS testing. NES and FDR = 0.05 adjusted p-values are indicated. (h) Boxplot showing log normalized counts of zebrafish igf1, igf2a, and igf2b expression in all melanocyte samples from bulk RNA-seq. N = 3 independent biological replicates. P-values calculated using DESeq2 and adjusted for FDR = 0.05. Box minima = 25th percentile, centre = 50th percentile, maxima = 75th percentile. Whiskers extends from the box maxima/minima to the largest/smallest value no further than 1.5 * IQR (interquartile range). Data beyond the end of the whiskers are plotted individually as outliers. (i− o) Cut & Run was performed on HOX13 expressing human melanoma cell lines, SKMEL-1088, SKMEL-1176, SKMEL-1206, using an antibody against HOXA13 and IgG as a negative control. (i) HOMER known motif analysis comparing HOXA13 vs IgG peaks identified significant enrichment for HOXB13 in all 3 melanoma cell lines. (j) GSEA pathway analysis using Cistrome-GO was performed, and the top enriched pathways are listed highlighting pathways related to insulin/IGF signaling. Insulin/IGF signaling pathways were identified using the search terms “insulin” and “IGF.” Insulin/IGF-related pathways were identified based on literature known to operate directly downstream of insulin/IGF signaling. A full list of pathways can be found in Supplementary Table 7. (k− o) IGV plot showing peaks along IGF1R, IGF2, IRS1, IRS2, and IGFBP3 for all 3 cell lines. The transcription start site (TSS) is indicated. (p) Patient derived melanoma cell lines were analyzed for HOXB13, pIGF1R, and total IGF1R expression by western blot. Actin was used as a loading control. Cell lines derived from acral and cutaneous tumors are labeled. SKMEL-1088 is a melanoma cell line of unknown anatomic origin. Images are representative of n = 3 biological replicates. (q) SKMEL-1176 and SKMEL-1206 acral melanoma cell lines were transfected with siRNAs targeting all 4 human HOX13 genes (HOXA13, HOXB13, HOXC13, HOXD13) or a non-targeting control. Cells were grown in standard media conditions. 96 h post-transfection, cells were collected and analyzed by western blot. HOXB13 was used to validate siRNA knockdown and then also probed for pIGF1R and IGF1R. Actin was used as a loading control. Blots are representative of n = 4 biological replicates. (r) Western blot quantification of the n = 4 biological replicates from (q). Data was analyzed using a two-sided student’s t-test. Error bars = SEM. For western blot source data, see Supplementary Fig. 4. * p-value < 0.05, ** p-value < 0.01, *** p-value < 0.001, **** p-value < 0.0001.
Related to Fig. 5. (a) Phospho-RTK array performed on WM3918 human melanoma cell line with or without overexpression of CRKL. Array tests phosphorylation status of 49 RTKs. For pRTK array source data, see Supplementary Fig. 2. (b) MeWo, SKMEL-1176, and 293T cells were transduced and selected to overexpress CRKL or an empty vector (EV) as a negative control. Western blot for CRKL, pIGF1R, and total IGF1R was then performed to compare basal levels of IGF signaling. Actin was used as a loading control. For western blot source data, see Supplementary Fig. 6. Quantification of western blots shown. Data represents n = 3 biological replicates and was analyzed using a two-sided student’s t-test. (c) Schematic representation of CRKL with a V5 tag. CRKL is composed of a SH2 (Src homology) domain that bind proteins with phospho-tyrosines and two SH3 domains that bind to proline-rich proteins. (d) Human melanoma cell line WM3918 overexpressing CRKL with a V5 tag was compared to CRKL overexpressing cells without the V5 tag. N = 5 control replicates and n = 6 CRKL-V5 biological replicates were used for each condition. Lysates were immunoprecipitated using a V5 antibody and then underwent mass spectrometry, yielding 57 significant interactors as defined by logFC > 2 and corrected p-value < 0.05. CRKL itself was among these significant interactors. Significant interactors with CRKL were organized based on whether they contain a canonical CRKL SH2-binding domain (pY-x-x-P), canonical CRKL SH3 binding motif (Ψ-P-Ψ-L/V/P/A/I-P-Ψ-K), known SH3 binding motif (proline-rich sequence), or no identified binding motif. Genes are color coded by their contribution to PI3K and MAPK signaling. (e) Heatmap showing increased detection of significant interactors in the CRKL-V5 vs control group, expressed as normalized counts. Raw data can be found in Supplementary Table 8. (f) Volcano plot showing log2 fold enrichment of CRKL-V5 IP vs. control IP (CRKL with no V5 tag). * p-value < 0.05, ** p-value < 0.01, *** p-value < 0.001, **** p-value < 0.0001.
Extended Data Fig. 10 CRKL-mediated melanocyte tailfin expansion depends on a HOX13/IGF positional program.
Related to Fig. 5. (a) Plasmids used to knockout the human CRKL transgene, and zebrafish IGF ligands (igf1, igf2a, igf2b) zebrafish HOX13 genes (hoxa13a, hoxa13b, hoxb13a, hoxc13a, hoxc13b, hoxd13a) and orthologue of HOXB7, hoxb7a, in a mosaic manner in CRKL stable line zebrafish. mitfa:Cas9 ensures melanocyte specificity in CRISPR editing. This plasmid also contains a fluorescent heart marker myl17:GFP, which was used to determine which fish had successful plasmid integration to be used in further downstream image analysis. (b) Surveyor validation demonstrating targeted editing of the human CRKL transgene using 3 different sgRNAs. For gel source data, see Supplementary Fig. 7a. (c) Surveyor validation demonstrating targeted editing zebrafish HOX genes using 6 different sgRNAs. Note, HOX13-targeting plasmids only target HOX13 genes (not hoxb7a) and hoxb7a-targeting plasmid only targets hoxb7a. For gel source data, see Supplementary Fig. 8. (d) Surveyor validation demonstrating targeted editing of all zebrafish IGF ligands using 3 different sgRNAs. For gel source data, see Supplementary Fig. 7b. (e) Tumor-free survival comparing acral to acral dnIRS2 genotypes. Number of fish for each genotype indicated in figure key. P-values generated by log-rank Mantel-Cox test.* p-value < 0.05, ** p-value < 0.01, *** p-value < 0.001, **** p-value < 0.0001.
Extended Data Fig. 11 HOX13 synergizes with CRKL to drive acral melanoma through IGF/PI3K signaling.
Related to Fig. 5. (a− d) Acral melanoma model imaged for melanocyte tailfin area at 3-days post-fertilization after indicated pharmacologic treatment. Representative images and quantification provided. P-value generated with two-sided student’s t-test. Error bars = SEM. (a) Insulin/IGF1 receptor antagonists BMS-754807 at 7.5 μM and NVP-AEW541 at 60 μM compared to 0.1% DMSO control. Data represents n = 35 DMSO-treated fish, n = 31 BMS-treated fish, and n = 26 NVP-treated fish pooled over n = 3 biological replicates. (b) PI3K inhibitor LY294002 at 15 μM and RAF/MEK inhibitor CH5126766 at 1 μM compared to 0.1% DMSO control. Data represents n = 45 DMSO-treated fish, n = 43 PI3K inhibitor-treated fish, and n = 21 RAF/MEK inhibitor-treated fish pooled over n = 3 biological replicates. (c) MEK inhibitors pimerasertib at 1 μM, refametinib at 1 μM, and trametinib at 200nM compared to 0.1% DMSO control. Data represents n = 46 DMSO-treated fish, n = 48 pimasertib-treated fish, and n = 61 refametinib-treated fish, and n = 44 trametinib-treated fish pooled over n = 4 biological replicates. (d) SOS1 inhibitor, Bl-3406, at 1 μM, MEK inhibitor refametinib at 1 μM, or combined Bl-3406 (1 μM) + refametinib (1 μM) treatment, compared to 0.1% DMSO control. Data represents n = 43 DMSO-treated fish, n = 31 SOS1 inhibitor-treated fish, n = 44 MEK inhibitor-treated fish, and n = 35 combined SOS1/MEK inhibitor-treated fish pooled over n = 3 biological replicates. * p-value < 0.05, ** p-value < 0.01, *** p-value < 0.001, **** p-value < 0.0001.
Related to Fig. 5. Melanocytes at different anatomic locations have different positional identities determined by HOX genes. Fin/limb melanocytes have higher expression of posterior HOX13 genes. HOX13 drives higher expression of IGF ligands and IGF1R, resulting in greater IGF signaling and increases the vulnerability of fin melanocytes to CRKL-mediated transformation. CRKL synergizes with a HOX13 program in fin melanocytes by forming a complex with PI3K, the primary downstream mediator of IGF1R, thereby amplifying IGF signaling. This results in tumor phenotypes with sensitivity to IGF1R or PI3K inhibition, but only modest effects from MAPK inhibition.
Raw gel source data related to the main and extended data figures. Red boxes indicate cropping used for associated figures.
CAMP criteria for diagnosis in zebrafish models of melanoma.
Tumour incidence anatomic distribution for all transgenic models, related to Figs. 1, 2, 5.
Zebrafish fin vs body RNA-seq. Related to Figs. 3, 5. Differential expression and pathway analysis provided. Differential expression calculated with DESeq2 using a two-sided Wald test and FDR = 0.05. GSEA pathway analysis calculated using weighted Kolmogorov–Smirnov testing and FDR = 0.05.
Human acral vs cutaneous RNA-seq. Related to Figs. 1, 4. Differential expression and pathway analysis provided. Differential expression calculated with DESeq2 using a two-sided Wald test and FDR = 0.05. GSEA pathway analysis calculated using weighted Kolmogorov–Smirnov testing and FDR = 0.05.
HOX13 ChIP–seq pathway analysis. Related to Fig. 5. Pathway analysis results provided. Cistrome-GO pathway analysis performed using minimum hypergeometric test and FDR = 0.05.
Human melanoma HOXA13 Cut&Run. Related to Fig. 5. MACS2 normalized peaks (HOXA13 normalized to IgG control) and pathway analysis results provided. Cistrome-GO pathway analysis performed using minimum hypergeometric test and FDR = 0.05.
CRKL immunoprecipitation–mass spectrometry. Related to Fig. 5. Raw data and data on significant interactors provided. Data were analysed with linear mixed-effects models using MSstatsTMT with FDR = 0.05. Significant interactors have Padj <0.05 and log2 fold change >2.
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Weiss, J.M., Hunter, M.V., Cruz, N.M. et al. Anatomic position determines oncogenic specificity in melanoma. Nature 604, 354–361 (2022). https://doi.org/10.1038/s41586-022-04584-6
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