Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Androgen receptor blockade promotes response to BRAF/MEK-targeted therapy

Nature volume 606, pages 797–803 (2022)Cite this article

Subjects

An Author Correction to this article was published on 10 January 2023

This article has been updated

Abstract

Treatment with therapy targeting BRAF and MEK (BRAF/MEK) has revolutionized care in melanoma and other cancers; however, therapeutic resistance is common and innovative treatment strategies are needed1,2. Here we studied a group of patients with melanoma who were treated with neoadjuvant BRAF/MEK-targeted therapy (NCT02231775, n = 51) and observed significantly higher rates of major pathological response (MPR; ≤10% viable tumour at resection) and improved recurrence-free survival (RFS) in female versus male patients (MPR, 66% versus 14%, P = 0.001; RFS, 64% versus 32% at 2 years, P = 0.021). The findings were validated in several additional cohorts2,3,4 of patients with unresectable metastatic melanoma who were treated with BRAF- and/or MEK-targeted therapy (n = 664 patients in total), demonstrating improved progression-free survival and overall survival in female versus male patients in several of these studies. Studies in preclinical models demonstrated significantly impaired anti-tumour activity in male versus female mice after BRAF/MEK-targeted therapy (P = 0.006), with significantly higher expression of the androgen receptor in tumours of male and female BRAF/MEK-treated mice versus the control (P = 0.0006 and P = 0.0025). Pharmacological inhibition of androgen receptor signalling improved responses to BRAF/MEK-targeted therapy in male and female mice (P = 0.018 and P = 0.003), whereas induction of androgen receptor signalling (through testosterone administration) was associated with a significantly impaired response to BRAF/MEK-targeted therapy in male and female patients (P = 0.021 and P < 0.0001). Together, these results have important implications for therapy.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Female patients with melanoma have an improved response to BRAF/MEK inhibition compared with male patients.
Fig. 2: Treatment with BRAF/MEK inhibition is associated with increased AR expression and phenotype is recapitulated in preclinical models.
Fig. 3: Modulation of AR activity is associated with differential response to BRAF/MEK-targeted therapy.

Similar content being viewed by others

Data availability

The additional datasets generated during and/or analysed during the current study of clinical trial NCT02231775 are available at the European Genome–Phenome Archive (EGAS00001006196). Other datasets generated during and/or analysed during the current study are available from the corresponding authors on reasonable request.

Change history

References

  1. Long, G. V. et al. Factors predictive of response, disease progression, and overall survival after dabrafenib and trametinib combination treatment: a pooled analysis of individual patient data from randomised trials. Lancet Oncol. 17, 1743–1754 (2016).

    Article  CAS  Google Scholar 

  2. Robert, C. et al. Five-year outcomes with dabrafenib plus trametinib in metastatic melanoma. New Engl. J. Med. 381, 626–636 (2019).

    Article  CAS  Google Scholar 

  3. Flaherty, K. T. et al. Improved survival with MEK inhibition in BRAF-mutated melanoma. N. Engl. J. Med. 367, 107–114 (2012).

    Article  CAS  Google Scholar 

  4. Long, G. V. et al. Neoadjuvant dabrafenib combined with trametinib for resectable, stage IIIB-C, BRAFV600 mutation-positive melanoma (NeoCombi): a single-arm, open-label, single-centre, phase 2 trial. Lancet Oncol. 20, 961–971 (2019).

    Article  CAS  Google Scholar 

  5. Leung, J. K. & Sadar, M. D. Non-genomic actions of the androgen receptor in prostate cancer. Front. Endocrinol. 8, 2 (2017).

    Article  Google Scholar 

  6. Pinto, J. A. et al. Gender and outcomes in non-small cell lung cancer: an old prognostic variable comes back for targeted therapy and immunotherapy? ESMO Open 3, e000344 (2018).

    Article  Google Scholar 

  7. Capone, I., Marchetti, P., Ascierto, P. A., Malorni, W. & Gabriele, L. Sexual dimorphism of immune responses: a new perspective in cancer immunotherapy. Front. Immunol. 9, 552 (2018).

    Article  Google Scholar 

  8. Clocchiatti, A. et al. Androgen receptor functions as transcriptional repressor of cancer-associated fibroblast activation. J. Clin. Invest. 128, 5531–5548 (2018).

    Article  Google Scholar 

  9. Pequeux, C. et al. Stromal estrogen receptor-alpha promotes tumor growth by normalizing an increased angiogenesis. Cancer Res. 72, 3010–3019 (2012).

    Article  CAS  Google Scholar 

  10. Zhao, L. et al. Pharmacological activation of estrogen receptor beta augments innate immunity to suppress cancer metastasis. Proc. Natl Acad. Sci. USA 115, E3673–E3681 (2018).

    Article  CAS  Google Scholar 

  11. Rothenberger, N. J., Somasundaram, A. & Stabile, L. P. The role of the estrogen pathway in the tumor microenvironment. Int. J. Mol. Sci. 19, 611 (2018).

    Article  Google Scholar 

  12. Ribeiro, M. P. C., Santos, A. E. & Custodio, J. B. A. The activation of the G protein-coupled estrogen receptor (GPER) inhibits the proliferation of mouse melanoma K1735-M2 cells. Chem. Biol. Interact. 277, 176–184 (2017).

    Article  CAS  Google Scholar 

  13. Qi, J. Therapy resistance by splicing: can the androgen receptor teach us about BRAF? Pigm. Cell Melanoma Res. 25, 293–294 (2012).

    Article  Google Scholar 

  14. Wang, Y. et al. Androgen receptor promotes melanoma metastasis via altering the miRNA-539-3p/USP13/MITF/AXL signals. Oncogene 36, 1644–1654 (2017).

    Article  CAS  Google Scholar 

  15. Natale, C. A. et al. Activation of G protein-coupled estrogen receptor signaling inhibits melanoma and improves response to immune checkpoint blockade. eLife 7, e31770 (2018).

    Article  Google Scholar 

  16. Smalley, K. S. Why do women with melanoma do better than men? eLife 7, e33511 (2018).

    Article  Google Scholar 

  17. Marzagalli, M. et al. Estrogen receptor beta in melanoma: from molecular insights to potential clinical utility. Front. Endocrinol. 7, 140 (2016).

    Article  Google Scholar 

  18. Amaria, R. N. et al. Neoadjuvant plus adjuvant dabrafenib and trametinib versus standard of care in patients with high-risk, surgically resectable melanoma: a single-centre, open-label, randomised, phase 2 trial. Lancet Oncol. 19, 181–193 (2018).

    Article  CAS  Google Scholar 

  19. Stacchiotti, S. et al. High-grade soft-tissue sarcomas: tumor response assessment-pilot study to assess the correlation between radiologic and pathologic response by using RECIST and Choi criteria. Radiology 251, 447–456 (2009).

    Article  Google Scholar 

  20. Eisenhauer, E. A. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur. J. Cancer 45, 228–247 (2008).

    Article  Google Scholar 

  21. Mendiratta, P. et al. Genomic strategy for targeting therapy in castration-resistant prostate cancer. J. Clin. Oncol. 27, 2022–2029 (2009).

    Article  CAS  Google Scholar 

  22. Schmidt, K. et al. The lncRNA SLNCR recruits the androgen receptor to EGR1-bound genes in melanoma and inhibits expression of tumor suppressor p21. Cell Rep 27, 2493–2507 (2019).

    Article  CAS  Google Scholar 

  23. Ma, M. et al. Sustained androgen receptor signaling is a determinant of melanoma cell growth potential and tumorigenesis. J. Exp. Med. 218, e20201137 (2021).

    Article  CAS  Google Scholar 

  24. Zhou, Z. X., Lane, M. V., Kemppainen, J. A., French, F. S. & Wilson, E. M. Specificity of ligand-dependent androgen receptor stabilization: receptor domain interactions influence ligand dissociation and receptor stability. Mol. Endocrinol. 9, 208–218 (1995).

    CAS  Google Scholar 

  25. Jin, H. J., Kim, J. & Yu, J. Androgen receptor genomic regulation. Transl. Androl. Urol. 2, 157–177 (2013).

    Google Scholar 

  26. Morvillo, V. et al. Atypical androgen receptor in the human melanoma cell line IIB-MEL-J. Pigm. Cell Res. 8, 135–141 (1995).

    Article  CAS  Google Scholar 

  27. Aguirre-Portoles, C. et al. ZIP9 Is a druggable determinant of sex differences in melanoma. Cancer Res. 81, 5991–6003 (2021).

    Article  CAS  Google Scholar 

  28. Liang, C. et al. TRIM36, a novel androgen-responsive gene, enhances anti-androgen efficacy against prostate cancer by inhibiting MAPK/ERK signaling pathways. Cell Death Dis. 9, 155 (2018).

    Article  Google Scholar 

  29. Li, S. et al. Activation of MAPK signaling by CXCR7 leads to enzalutamide resistance in prostate cancer. Cancer Res. 79, 2580–2592 (2019).

    Article  CAS  Google Scholar 

  30. Zhang, M. et al. Targeting AR-Beclin 1 complex-modulated growth factor signaling increases the antiandrogen enzalutamide sensitivity to better suppress the castration-resistant prostate cancer growth. Cancer Lett. 442, 483–490 (2019).

    Article  CAS  Google Scholar 

  31. Wu, H. et al. Combination of sorafenib and enzalutamide as a potential new approach for the treatment of castration-resistant prostate cancer. Cancer Lett. 385, 108–116 (2017).

    Article  CAS  Google Scholar 

  32. Kuser-Abali, G., Alptekin, A., Lewis, M., Garraway, I. P. & Cinar, B. YAP1 and AR interactions contribute to the switch from androgen-dependent to castration-resistant growth in prostate cancer. Nat. Commun. 6, 8126 (2015).

    Article  ADS  Google Scholar 

  33. Lin, L. et al. The Hippo effector YAP promotes resistance to RAF- and MEK-targeted cancer therapies. Nat. Genet. 47, 250–256 (2015).

    Article  CAS  Google Scholar 

  34. Rubin, J. B. et al. Sex differences in cancer mechanisms. Biol. Sex Differ. 11, 17 (2020).

    Article  CAS  Google Scholar 

  35. Boese, A. C., Kim, S. C., Yin, K. J., Lee, J. P. & Hamblin, M. H. Sex differences in vascular physiology and pathophysiology: estrogen and androgen signaling in health and disease. Am. J. Physiol. Heart. Circ. Physiol. 313, H524–H545 (2017).

    Article  Google Scholar 

  36. Guan, X. et al. Androgen receptor activity in T cells limits checkpoint blockade efficacy. Nature https://doi.org/10.1038/s41586-022-04522-6 (2022).

  37. Balzano, S. et al. The effect of androgen blockade on pulsatile gonadotrophin release and LH response to naloxone. Clin. Endocrinol. 27, 491–499 (1987).

    Article  CAS  Google Scholar 

  38. Kerrigan, J. R., Veldhuis, J. D. & Rogol, A. D. Androgen-receptor blockade enhances pulsatile luteinizing hormone production in late pubertal males: evidence for a hypothalamic site of physiologic androgen feedback action. Pediatr. Res. 35, 102–106 (1994).

    Article  CAS  Google Scholar 

  39. Maughan, B. L. & Antonarakis, E. S. Enzalutamide in chemo-naive castration-resistant prostate cancer: effective for most but not for all. Asian J. Androl. 16, 807–808 (2014).

    Article  CAS  Google Scholar 

  40. Dehm, S. M., Schmidt, L. J., Heemers, H. V., Vessella, R. L. & Tindall, D. J. Splicing of a novel androgen receptor exon generates a constitutively active androgen receptor that mediates prostate cancer therapy resistance. Cancer Res. 68, 5469–5477 (2008).

    Article  CAS  Google Scholar 

  41. Arora, V. K. et al. Glucocorticoid receptor confers resistance to antiandrogens by bypassing androgen receptor blockade. Cell 155, 1309–1322 (2013).

    Article  CAS  Google Scholar 

  42. Cross, T. L., Kasahara, K. & Rey, F. E. Sexual dimorphism of cardiometabolic dysfunction: gut microbiome in the play? Mol. Metab. 15, 70–81 (2018).

    Article  CAS  Google Scholar 

  43. Martin, A. M., Sun, E. W., Rogers, G. B. & Keating, D. J. The influence of the gut microbiome on host metabolism through the regulation of gut hormone release. Front. Physiol. 10, 428 (2019).

    Article  Google Scholar 

  44. Sudo, N. Microbiome, HPA axis and production of endocrine hormones in the gut. Adv. Exp. Med. Biol. 817, 177–194 (2014).

    Article  CAS  Google Scholar 

  45. Gaballa, R. et al. Exosomes-mediated transfer of Itga2 promotes migration and invasion of prostate cancer cells by inducing epithelial-mesenchymal transition. Cancers 12, 2300 (2020).

    Article  CAS  Google Scholar 

  46. Ricke, E. A. et al. Androgen hormone action in prostatic carcinogenesis: stromal androgen receptors mediate prostate cancer progression, malignant transformation and metastasis. Carcinogenesis 33, 1391–1398 (2012).

    Article  CAS  Google Scholar 

  47. Scatena, C. et al. Androgen receptor expression inversely correlates with histological grade and N stage in ER+/PgRlow male breast cancer. Breast Cancer Res. Treat. 182, 55–65 (2020).

    Article  CAS  Google Scholar 

  48. Wang, D. & Tindall, D. J. Androgen action during prostate carcinogenesis. Methods Mol. Biol. 776, 25–44 (2011).

    Article  CAS  Google Scholar 

  49. Xia, N., Cui, J., Zhu, M., Xing, R. & Lu, Y. Androgen receptor variant 12 promotes migration and invasion by regulating MYLK in gastric cancer. J. Pathol. 248, 304–315 (2019).

    CAS  Google Scholar 

  50. McQuade, J. L. et al. Association of body-mass index and outcomes in patients with metastatic melanoma treated with targeted therapy, immunotherapy, or chemotherapy: a retrospective, multicohort analysis. Lancet Oncol. 19, 310–322 (2018).

    Article  Google Scholar 

  51. Robert, C. et al. Five-year outcomes from a phase 3 METRIC study in patients with BRAF V600 E/K-mutant advanced or metastatic melanoma. Eur. J. Cancer 109, 61–69 (2019).

    Article  CAS  Google Scholar 

  52. Andrews, S. FastQC: a quality control tool for high throughput sequence data. Version 0.11.9. http://www.bioinformatics.babraham.ac.uk/projects/fastqc (2010).

  53. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  CAS  Google Scholar 

  54. DeLuca, D. S. et al. RNA-SeQC: RNA-seq metrics for quality control and process optimization. Bioinformatics 28, 1530–1532 (2012).

    Article  CAS  Google Scholar 

  55. Anders, S., Pyl, P. T. & Huber, W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).

    Article  CAS  Google Scholar 

  56. Korotkevich, G. et al. Fast gene set enrichment analysis. Preprint at bioRxiv https://doi.org/10.1101/060012 (2021).

  57. Efstathiou, E. et al. Enzalutamide in combination with abiraterone acetate in bone metastatic castration-resistant prostate cancer patients. Eur. Urol. Oncol. 3, 119–127 (2020).

    Article  Google Scholar 

  58. Maity, S. N. et al. Targeting of CYP17A1 lyase by VT-464 inhibits adrenal and intratumoral androgen biosynthesis and tumor growth of castration resistant prostate cancer. Sci. Rep. 6, 35354 (2016).

    Article  ADS  CAS  Google Scholar 

Download references

Acknowledgements

J.A.W. is supported by the NIH (1 R01 CA219896-01A1), the Melanoma Research Alliance (4022024), the American Association for Cancer Research Stand Up To Cancer (SU2C-AACR-IRG-19-17) and the MD Anderson Cancer Center’s Melanoma Moon Shots Program. J.L.M. is supported by the Melanoma Research Alliance, an American Society of Clinical Oncology and Conquer Cancer Foundation Career Development Award, the Elkins Foundation, the Seerave Foundation, the Rising Tide Foundation, the Mark Foundation, an MD Anderson Cancer Center (MDACC) Melanoma SPORE Developmental Research Program Award and the MD Anderson Physician Scientist Program, and acknowledges the Transdisciplinary Research in Energetics and Cancer Research Training Workshop R25CA203650 and the MDACC Center for Energy Balance in Cancer Prevention and Survivorship. A.P.C. is supported by the CPRIT Research Training Program at the MD Anderson Cancer Center (RP170067), The US Department of State, Bureau of Educational and Cultural Affairs, and the Fulbright Franco-Américaine Commission. M.C.A. is supported by a National Health and Medical Research Council of Australia CJ Martin Early Career Fellowship (1148680). B.H. was supported by the National Institutes of Health (T32 CA 009599) and the MD Anderson Cancer Center support grant (P30 CA016672). M.G.W. is supported by the National Institutes of Health (T32 CA 009599) and the MD Anderson Cancer Center support grant (P30 CA016672). R.G.W. is supported by the National Institutes of Health (T32 CA 009599) and the MD Anderson Cancer Center support grant (P30 CA016672). G.M. is supported by the National Institute of Health (1F32CA260769-01). M.A.D. is supported by the Dr Miriam and Sheldon G. Adelson Medical Research Foundation, the AIM at Melanoma Foundation, the NIH/NCI (1 P50 CA221703-02), Cancer Fighters of Houston, the Anne and John Mendelsohn Chair for Cancer Research, and philanthropic contributions to the Melanoma Moon Shots Program of MD Anderson. G.O. is supported by the National Institutes of Health (T32 CA 009599) and the MD Anderson Cancer Center support grant (P30 CA016672). The University of Texas MD Anderson Cancer Center Science Park Next-Generation Sequencing (NGS) Facility is supported by CPRIT Core Facility Support Awards (CPRIT RP170002).

Author information

Author notes

  1. Miles C. Andrews

    Present address: Department of Medicine, Central Clinical School, Monash University, Melbourne, Victoria, Australia

  2. Gabriel Ologun

    Present address: Department of Surgery, Guthrie Courtland Medical Center, Courtland, NY, USA

  3. Alexandria P. Cogdill

    Present address: Immunai, New York, NY, USA

  4. Peter Prieto

    Present address: Department of Surgery, University of Rochester, Rochester, NY, USA

  5. Beth Helmink

    Present address: Department of Surgery, Washington University in St Louis, St Louis, MO, USA

  6. Patrick Hwu

    Present address: Moffitt Cancer Center, Tampa, FL, USA

  7. Zachary A. Cooper

    Present address: AstraZeneca, Gaithersburg, MD, USA

  8. Michael T. Tetzlaff

    Present address: Department of Pathology, University of California, San Francisco, CA, USA

  9. These authors contributed equally: Christopher P. Vellano, Michael G. White, Miles C. Andrews, Manoj Chelvanambi, Russell G. Witt

Authors and Affiliations

  1. TRACTION Platform, Therapeutics Discovery Division, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Christopher P. Vellano, Joseph R. Daniele, Jeffrey J. Kovacs, Michael Peoples, Guang Gao, Mikhila Mahendra, XiaoYan Ma, Ningping Feng, Lorenzo Federico, Timothy P. Heffernan & Joseph R. Marszalek

  2. Department of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Michael G. White, Miles C. Andrews, Manoj Chelvanambi, Russell G. Witt, Elizabeth M. Burton, Matthew J. Lastrapes, Gabriel Ologun, Alexandria P. Cogdill, Golnaz Morad, Peter Prieto, M. A. Wadud Khan, Beth Helmink, Jeffrey E. Lee, Zachary A. Cooper, Jeffrey E. Gershenwald, Anthony Lucci, Emily Z. Keung, Merrick I. Ross, Lauren E. Haydu, Zachary Kulstad, Sarah Johnson & Jennifer A. Wargo

  3. Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Mark Titus & Christopher Logothetis

  4. Department of Melanoma Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Jennifer L. McQuade, Michael A. Davies, Rodabe N. Amaria, Sapna Patel, Patrick Hwu & Hussein A. Tawbi

  5. Division of Melanoma, Sarcomas, and Rare Tumors, European Institute of Oncology, IRCCS, Milan, Italy

    Fabio Conforti & Laura Pala

  6. Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Alexander J. Lazar & Michael T. Tetzlaff

  7. Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Alexander J. Lazar, Yanshuo Chu, Guangchun Han, Scott E. Woodman, Haifeng Zhu, P. Andrew Futreal, Linghua Wang & Jennifer A. Wargo

  8. Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Alexander J. Lazar, Rossana Lazcano Segura, Khalida Wani & Courtney W. Hudgens

  9. Clinical Development and Analytics, Novartis Pharmaceuticals, East Hanover, NJ, USA

    Hiya Banerjee

  10. Novartis Pharma, Basel, Switzerland

    Mike Lau

  11. Department of Statistics and Quantitative Methods, University of Milan-Bicocca, Milan, Italy

    Eleonora Pagan

  12. Department of Tumor Biology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA

    Qian Liu & Eric Lau

  13. Department of Gynecologic Oncology and Reproductive Medicine, University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Mikayla S. Borthwick, Melinda S. Yates & Shannon N. Westin

  14. Melanoma Institute Australia, The University of Sydney, and Royal North Shore and Mater Hospitals, Sydney, New South Wales, Australia

    Georgina V. Long

  15. Department of Genetics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Swathi Arur

  16. Cell, Development & Cancer Biology, Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA

    Amy E. Moran

Authors
  1. Christopher P. Vellano
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Michael G. White
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Miles C. Andrews
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Manoj Chelvanambi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Russell G. Witt
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Joseph R. Daniele
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Mark Titus
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Jennifer L. McQuade
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Fabio Conforti
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Elizabeth M. Burton
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Matthew J. Lastrapes
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Gabriel Ologun
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Alexandria P. Cogdill
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Golnaz Morad
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Peter Prieto
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Alexander J. Lazar
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Yanshuo Chu
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Guangchun Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. M. A. Wadud Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Beth Helmink
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Michael A. Davies
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Rodabe N. Amaria
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Jeffrey J. Kovacs
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Scott E. Woodman
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Sapna Patel
    View author publications

    You can also search for this author in PubMed Google Scholar

  26. Patrick Hwu
    View author publications

    You can also search for this author in PubMed Google Scholar

  27. Michael Peoples
    View author publications

    You can also search for this author in PubMed Google Scholar

  28. Jeffrey E. Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  29. Zachary A. Cooper
    View author publications

    You can also search for this author in PubMed Google Scholar

  30. Haifeng Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  31. Guang Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  32. Hiya Banerjee
    View author publications

    You can also search for this author in PubMed Google Scholar

  33. Mike Lau
    View author publications

    You can also search for this author in PubMed Google Scholar

  34. Jeffrey E. Gershenwald
    View author publications

    You can also search for this author in PubMed Google Scholar

  35. Anthony Lucci
    View author publications

    You can also search for this author in PubMed Google Scholar

  36. Emily Z. Keung
    View author publications

    You can also search for this author in PubMed Google Scholar

  37. Merrick I. Ross
    View author publications

    You can also search for this author in PubMed Google Scholar

  38. Laura Pala
    View author publications

    You can also search for this author in PubMed Google Scholar

  39. Eleonora Pagan
    View author publications

    You can also search for this author in PubMed Google Scholar

  40. Rossana Lazcano Segura
    View author publications

    You can also search for this author in PubMed Google Scholar

  41. Qian Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  42. Mikayla S. Borthwick
    View author publications

    You can also search for this author in PubMed Google Scholar

  43. Eric Lau
    View author publications

    You can also search for this author in PubMed Google Scholar

  44. Melinda S. Yates
    View author publications

    You can also search for this author in PubMed Google Scholar

  45. Shannon N. Westin
    View author publications

    You can also search for this author in PubMed Google Scholar

  46. Khalida Wani
    View author publications

    You can also search for this author in PubMed Google Scholar

  47. Michael T. Tetzlaff
    View author publications

    You can also search for this author in PubMed Google Scholar

  48. Lauren E. Haydu
    View author publications

    You can also search for this author in PubMed Google Scholar

  49. Mikhila Mahendra
    View author publications

    You can also search for this author in PubMed Google Scholar

  50. XiaoYan Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  51. Christopher Logothetis
    View author publications

    You can also search for this author in PubMed Google Scholar

  52. Zachary Kulstad
    View author publications

    You can also search for this author in PubMed Google Scholar

  53. Sarah Johnson
    View author publications

    You can also search for this author in PubMed Google Scholar

  54. Courtney W. Hudgens
    View author publications

    You can also search for this author in PubMed Google Scholar

  55. Ningping Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

  56. Lorenzo Federico
    View author publications

    You can also search for this author in PubMed Google Scholar

  57. Georgina V. Long
    View author publications

    You can also search for this author in PubMed Google Scholar

  58. P. Andrew Futreal
    View author publications

    You can also search for this author in PubMed Google Scholar

  59. Swathi Arur
    View author publications

    You can also search for this author in PubMed Google Scholar

  60. Hussein A. Tawbi
    View author publications

    You can also search for this author in PubMed Google Scholar

  61. Amy E. Moran
    View author publications

    You can also search for this author in PubMed Google Scholar

  62. Linghua Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  63. Timothy P. Heffernan
    View author publications

    You can also search for this author in PubMed Google Scholar

  64. Joseph R. Marszalek
    View author publications

    You can also search for this author in PubMed Google Scholar

  65. Jennifer A. Wargo
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

C.P.V., M.G.W., M.C.A. and J.A.W. formulated research goals and aims. C.P.V., M.G.W., M.C.A., T.P.H., J.L.M., J.R.M. and J.A.W. developed and designed methodology. M.G.W., M.C.A., L.W., H.Z., S.E.W., M.J.L., L.E.H., G.H. and Y.C. designed and executed pipelines and analytical code. C.P.V., M.G.W., R.G.W., M.C.A., H.Z., F.C., S.E.W., M.J.L., M.C., M.A.W.K., H.B., E.P. and L.P. conducted statistical analysis, built models and plotted results. C.P.V., J.R.M., J.L.M., M.T.T., M.G.W., M.T., C.L., J.R.D., M.M., R.G.W., Z.A.C., G.O., A.P.C., G.M., P.P., E.M.B., B.H., J.J.K., R.L.S., X.M. and Z.K. designed and conducted immunological assays and preclinical studies, generated and annotated metadata and created cell lines. J.L.M., P.A.F., M.A.D., R.N.A., H.A.T., S.P., P.H., J.E.L., J.E.G., A.L., E.Z.K., M.I.R., A.J.L., K.W., C.W.H. and G.V.L. provided clinical samples. J.J.K., S.J., N.F., G.G., E.L., Q.L. and M.S.B. performed in vitro studies. C.P.V. and J.R.M. were scientific drivers, and planned and oversaw all mouse experiments and studies. M.P. generated of AR-knockout cell lines. M.G.W., C.P.V., M.C.A., L.F., J.R.D., J.L.M., J.A.W., R.G.W., M.C., S.A., M.S.B., M.S.Y. and S.N.W. prepared the manuscript. A.E.M., T.P.H., J.R.M. and J.A.W. provided scientific oversight. J.A.W. supervised and oversaw all aspects of study including design, conduct and analyses. All of the authors reviewed and edited the manuscript.

Corresponding authors

Correspondence to Timothy P. Heffernan, Joseph R. Marszalek or Jennifer A. Wargo.

Ethics declarations

Competing interests

J.A.W. is listed as an inventor on a US patent application (PCT/US17/53.717); reports compensation for speaker’s bureau and honoraria from Imedex, Dava Oncology, Omniprex, Illumina, Gilead, PeerView, MedImmune and Bristol-Myers Squibb (BMS); serves as a consultant/advisory board member for Roche/Genentech, Novartis, AstraZeneca, GlaxoSmithKline (GSK), BMS, Merck, Biothera Pharmaceuticals and Micronoma; and holds stock options from Micronoma. J.L.M. serves as a consultant for Merck and is on the advisory board for BMS. Z.A.C. is a current employee of and owns stock in AstraZeneca. M.C.A. reports advisory board participation and honoraria from MSD Australia outside the current work; received research funds to their institution from MSD Australia and BMS Australia outside the current work; and is a named co-inventor on patent applications relating to methods and compositions for treating cancer (WO2020106983A1) unrelated to the current work, and methods for improving sex-dimorphic responses to targeted therapy in melanoma (US20200164034A1) related to the current work. R.N.A. receives research support and advisory boards for Novartis, BMS, Iovance and research support from Genentech. C.L. serves as a consultant/advisory board for Merck, Sharp & Dohme, Exelixis, Bayer and Amgen; reports honoraria from Merck, Sharp & Dohme, Bayer and Amgen; and clinical grants from Janssen, ORIC Pharmaceuticals, Novartis and Aragon Pharmaceuticals. M.A.D. is an advisory board member/consultant for BMS, Novartis, Array, Roche/Genentech, GSK, Sanofi-Aventis and ABM Therapeutics; principal investigator of a research grant to their institution from Astrazeneca, Roche/Genentech, GSK, Myriad, Oncothyreon, Sanofi-Aventis and ABM Therapeutics. P.A.F. serves on the scientific advisory board for Scorpion Therapeutics. A.E.M. has a sponsored research agreement with MedImmune/AstraZeneca. S.N.W. serves as a consultant for Agenus, AstraZeneca, Caris, Clovis Oncology, Eisai, EQRX, GSK/Tesaro, ImmunoGen, Lilly, Merck, Pfizer, Mersana, Roche/Genentech and Zentalis; she receives research support to her institution from AstraZeneca, Bayer, Bio-Path, Clovis Oncology, Cotinga Pharmaceuticals, GSK/Tesaro, Mereo, Novartis, OncXerna, Roche/Genentech and Zentalis. A.J.L. has served on advisory boards and/or consulted for AbbVie, AstraZeneca, Bayer, BMS, Deciphera Pharmaceuticals, Foghorn Therapeutics, GSK, Invitae, Illumina, Iterion Therapeutics, Merck, Novartis, Nucleai, Paige.AI, Pfizer, Roche/Genentech and ThermoFisher. G.V.L. is a consultant advisor for Agenus, Amgen, Array Biopharma, Boehringer Ingelheim International, Bristol Myers Squibb, Evaxion Biotech A/S, Hexal (Sandoz Company), Highlight Therapeutics S.L., Merck Sharpe & Dohme (Australia) Pty Limited, Novartis Pharma, OncoSec Medical Australia, Pierre Fabre, Provectus, Qbiotics Group Limited, Regeneron Pharmaceuticals. The other authors declare no competing interests.

Peer review

Peer review information

Nature thanks Caroline Robert, Joshua Rubin and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 Expanded analysis of clinical cohorts.

A) Rates of major pathologic response (MPR) based on menopausal status of female patients within the neoadjuvant cohort (p = 0.79) by logistic regression. B) Waterfall plot of percent change in tumour size by cross-sectional imaging in the neoadjuvant cohort (p = 0.54). C) Waterfall plot of percent change in tumour size by cross-sectional imaging of the pooled neoadjuvant and metastatic cohorts (p = 0.02).

Extended Data Fig. 2 Analysis of published cohorts of BRAF/MEK inhibited patients.

A) Schema of metastatic BRAF inhibitor monotherapy clinical cohort of patients treated with targeted therapy and their clinical outcomes studied. B) Progression free survival by sex in the metastatic BRAF inhibitor monotherapy clinical cohort (n = 212, hazard ratio 1.2 CI [0.88-1.6] p = 0.241, by Kaplan-Meier method). C) Overall survival by sex in the metastatic BRAF inhibitor monotherapy clinical cohort (n = 212, hazard ratio 1.32 CI [0.95-1.82] p = 0.095, by Kaplan-Meier method). D) Schema of metastatic MEK inhibitor monotherapy clinical cohort of patients treated with targeted therapy and their clinical outcomes studied. E) Progression free survival by sex in the metastatic MEK inhibitor monotherapy clinical cohort (n = 206, hazard ratio 1.35 CI [1.01-1.81] p = 0.043, by Kaplan-Meier method). F) Overall survival by sex in the metastatic MEK inhibitor monotherapy clinical cohort (n = 206, hazard ratio 1.61 CI [1.19-2.18] p = 0.002, by Kaplan-Meier method). G) Schema of second neoadjuvant clinical cohort of patients treated with targeted therapy and their clinical outcomes studied. H) Major pathologic response defined as ≤ 10% viable tumour in females versus males (p = 0.92, by Chi-squared). I) Recurrence free survival by sex in the second neoadjuvant cohort (n = 40, hazard ratio 1.16, 95% CI [0.36–2.07], p = 0.733, by Kaplan-Meier method). J) Kaplan Meier survival curves of overall survival by sex of melanoma patients abstracted with The Cancer Genome Atlas (p = 0.373 by univariate analysis, p = 0.369 controlling for age at diagnosis and stage). K) Paired clinical samples of circulating testosterone prior to initiation and after treatment with BRAF/MEK targeted therapy (p = 0.9453 and 0.2036 respectively by two-sided Student’s t-test). L) AR staining pre-treatment in males (blue) and females (pink) by MPR (p = 0.72, by two-sided unpaired t-test).

Extended Data Fig. 3 Transcriptomic analysis of clinical specimens.

A) Heatmap of AR signature genes of on-treatment specimens demonstrating upregulation of AR signature pathways. B) Volcano plot of differentially expressed genes after whole transcriptomic analysis of on-treatment with BRAF/MEK inhibition clinical specimens. Vertical dotted blue lines represent log foldchange greater than 2. Horizontal dotted blue line represents q < 0.05. Genes with log fold change greater than 5 and q < 0.05 are labelled. C) Heatmap of AR signature genes of pre-treatment specimens of AR signature pathways. D) Volcano plot of differentially expressed genes after whole transcriptomic analysis of pre-treatment with BRAF/MEK inhibition clinical specimens. E) Androgen signalling score comparing clinical samples achieving an MPR and those not achieving an MPR for those on treatment (ON, <MPR n = 2, MPR n = 4), at the time of resection (SURG, <MPR n = 9, MPR = 7; p = 0.066), aggregate of those on treatment and surgically resected (ONandSURG, <MPR = 11, MPR  = 11; p = 0.011), and samples collected prior to treatment (PRE, <MPR n = 4, MPR n = 5; p = 0.95); groups were compared using a two-sided Student’s t test. Box plot represents the median bar with the box bounding interquartile range (IQR) and whiskers to the most extreme point within 1.5 x IQR.

Extended Data Fig. 4 Murine model of melanoma validates a sexually dimorphic response and suggests AR activity as a mechanism of resistance.

A-B) Percent change in tumour volume for male and female C57BL/6 mice implanted subcutaneously with BP cells that were treated with Vehicle or BRAF/MEKi (n = 10 mice per group; A – mice aged 9 weeks, B - mice aged 12 weeks). Results from the second and third repeats of this experiment are shown in A and B, respectively (p = 0.039 and p = 0.45). C) Percent change in tumour volume in BP injected C57BL/6 male mice treated with vehicle in the presence or absence of BRAF/MEKi with endocrine modulation through androgen blockade with enzalutamide or castration (mice aged 14 weeks). All tumour growth curves were compared by ANOVA with multiple comparisons (n = 10/group except BRAF/MEKi + castration where n = 9; p = 0.003 BRAF/MEKi vs BRAF/MEKi + Enzalutamide; p = 0.031 BRAF/MEKi vs BRAF/MEKi + Castration). D-E) Percent change in tumour volume for AR-KO BP tumours in female (D) (p = 0.99) and male (E) (p = 0.98) CD-1 mice treated with vehicle or BRAF/MEKi in combination with either testosterone or enzalutamide, respectively (n = 10 mice/group; aged 11 weeks). All tumour growth represented as mean + SEM and p-values were calculated using ANOVA with multiple comparisons.

Extended Data Fig. 5 Mechanistic and Validations Murine Studies Tumour Volume Curves.

A) Tumour volumes for male and female C57BL/6 mice implanted subcutaneously with YUMMER 1.7 cells treated with Vehicle or BRAF/MEK inhibition (p = 0.06 between male and female BRAF/MEKi; 30 mpk dabrafenib and 1 mpk trametinib, PO, QD). n = 10 mice per group, aged 12-13 weeks. B-D) Percent change in tumour volume for male and female C57BL/6 mice implanted subcutaneously with BP (BRAFV600E, PTEN−/−) cells (p = 0.003, 0.26, and 0.93) between male and female BRAF/MEKi. Mice were treated as in A. n = 10 mice per group; aged 12-13 weeks. The experiment was performed in triplicate with each experiment represented above. E) Tumour volumes for male and female CD-1 nude mice implanted and treated as in A (p = 0.01 between male and female BRAF/MEKi). n = 10 mice per group; aged 11 weeks. F) Tumour volumes of male and female CD-1 mice with AR-KO BP tumours treated with vehicle or BRAF/MEKi (p = 0.317 between male and female BRAF/MEKi); n = 10/group; aged 11 weeks. All tumour growth represented as mean + SEM and p-values were calculated using ANOVA with multiple comparisons.

Extended Data Fig. 6 AR gene and protein expression analysis of preclinical models and serum testosterone measurements of clinical specimens.

A) AR gene expression of murine BP tumours from male and female mice treated with either Vehicle or BRAFi/MEKi (female Vehicle vs Female BRAF/MEKi treated, p = 0.005, male Vehicle vs male BRAF/MEKi treated, p = 0.16) p-values were calculated using two-sided Student’s t-test B) AR immunofluorescence staining of samples from female and male mice treated with Vehicle, BRAF/MEKi, BRAF/MEKI + Testosterone (females, vehicle vs BRAF/MEKi, p = 0.005) and BRAF/MEKi + enzalutamide (males, vehicle vs BRAF/MEKi, p = 0.16). p-values were calculated by two-sided Student’s t-test. C) Plasma testosterone levels for male mice across treatment groups. Decreased plasma testosterone was noted in both the castration group as compared to Vehicle (p = 0.039) as well as the BRAF/MEKi + castration group as compared to the BRAF/MEKi group (p = 0.017). p-values were calculated using a Kruskal Wallis test. D) Plasma testosterone levels for female mice across treatment groups. Increased plasma testosterone was noted in the Vehicle + testosterone group as compared to the BRAF/MEKi group (p = 0.007) and vehicle group (p = 0.007). Similarly increased testosterone was noted in the BRAF/MEKi + testosterone group as compared to the vehicle group (p = 0.009) and BRAF/MEKi group (p = 0.009). No other associations were significant p < 0.05 by Kruskal Wallis test. E) AR immunofluorescence staining of AR-KO BP tumour samples from female and male mice treated with either Vehicle or BRAFi/MEKi. p-values were calculated by Student’s t-test. Histograms in A and B represent mean + SD whereas C and D represent mean + SEM.

Extended Data Fig. 7 Modulation of AR activity is associated with differential response to BRAF/MEK inhibition.

A) Percent change in tumour volume in BP injected C57BL/6 female mice treated with vehicle in the presence or absence of BRAF/MEKi with endocrine modulation with either estradiol or estradiol and oophorectomy (n = 10/group; mice aged 14 weeks). No significant effects exist within either cohort as calculated by ANOVA. B) Percent change in tumour volume for female C57BL/6 mice implanted subcutaneously with YUMMER 1.7 cells treated with BRAF/MEKi or BRAF/MEKi + testosterone (n = 10/group; aged 13 weeks, p = 0.0003 between BRAF/MEKi and BRAF/MEKi + testosterone). C) Percent change in tumour volume in YUMMER1.7 injected into C57BL/6 male mice treated with vehicle in the presence or absence of BRAF/MEKi with endocrine modulation with enzalutamide (n = 10/group; mice aged 14 weeks, p = 0.031). D) Heatmap of differentially expressed androgen responsive genes between high versus low testosterone groups q < 0.05. Groups by androgen staining levels, sex, and treatment. E) Percent change in BP tumour volume in male C57BL/6 mice treated with vehicle in the presence or absence of BRAF/MEKi with physical castration or BRAF/MEKi with castration and exogenous testosterone (n = 10/group; aged 14 weeks, p = 0.01 for BRAF/MEKi + castration versus BRAF/MEKi, p = 0.04 for BRAF/MEKi versus BRAF/MEKi + castration + testosterone, and p = 0.0004 for BRAF/MEKi + castration vs BRAF/MEKi + castration + testosterone). F) Quantification of the percent of AR+ nuclei by immunofluorescence in BP tumours from male mice treated with vehicle (n = 7), BRAF/MEKi (n = 5), or BRAF/MEKi + enzalutamide (n = 7) (p = 0.003 between BRAF/MEKi and BRAF/MEKi + enzalutamide). p-values were calculated using two-sided Student’s t-test. G) Quantification of the percent of AR+ nuclei by immunofluorescence in BP tumours from female mice treated with vehicle (n = 7), BRAF/MEKi (n = 7), BRAF/MEKi + testosterone (n = 5) (p = 0.006 between vehicle and BRAF/MEKi and p = 0.003 between BRAF/MEKi and BRAF/MEKi + testosterone in female mice). p-values were calculated using two-sided Student’s t-test. Tumour growth curves in panels A-C and E represent mean + SEM. Histograms in panels F and G represent mean + SEM. All tumour growth curves were compared by ANOVA with multiple comparisons.

Extended Data Fig. 8 Interventional Murine Studies Tumour Volumes.

A) Tumour volumes curves of BP tumour growth in male mice treated with vehicle (n = 10), BRAF/MEKi alone (n = 9) or BRAF/MEKi in combination with testosterone (n = 9) (p = 0.12). B) Tumour volumes curves of BP tumour growth in female mice treated with vehicle, BRAF/MEKi alone or BRAF/MEKi in combination with testosterone (p = 0.003). (n = 10 mice/group). C) Tumour volume curves of BP tumour growth in male mice treated with BRAF/MEKi alone or BRAF/MEKi in combination with AR blockade (p = 0.02) or AR blockade with testosterone (p = 0.07) (n = 10 mice/group). D) Tumour volume curves of BP tumour growth in female mice treated with BRAF/MEKi alone or BRAF/MEKi in combination with AR blockade (p = 0.002) or AR blockade with testosterone (p = 0.34) (n = 10 mice/group). E) Tumour volume curves of BP AR KO tumour growth in CD-1 nude female mice treated with vehicle in the presence or absence of BRAF/MEKi with endocrine modulation (mice aged 14 weeks). BRAF/MEKi vs BRAF/MEKi + testosterone, p = 0.99; (n = 10 mice/group). F) Tumour volume curves of AR KO BP injected CD-1 nude male mice treated with vehicle in the presence or absence of BRAF/MEKi or BRAF/MEKi + enzalutamide (p = 0.98) (n = 10 mice/group). G) Tumour volume curves of YUMMER 1.7 tumour growth in male mice treated with vehicle, BRAF/MEKi or BRAF/MEKi + AR blockade (p = 0.12) (n = 10 mice/group) H) Tumour volume curves of BP tumour growth in C57BL/6 male mice treated with BRAF/MEKi, BRAF/MEKi + castration (p = 0.01) or BRAF/MEKi + castration + testosterone (p = 0.0005) (n = 10 mice/group). I) Change in tumour volume in BP injected C57BL/6 male mice treated with vehicle in the presence or absence of BRAF/MEKi with endocrine modulation through either androgen blockade, androgen blockade with testosterone, estradiol, or castration (mice aged 14 weeks) (n = 10 mice/group). J) Tumour volume curves of YUMMER 1.7 tumour growth in female mice treated with vehicle, BRAF/MEKi or BRAF/MEKi + testosterone (p = 0.0005) (n = 10 mice/group). K) Change in tumour volume in BP injected C57BL/6 female mice treated with vehicle in the presence or absence of BRAF/MEKi with endocrine modulation with either estradiol or estradiol and oophorectomy (n = 10 mice/group; mice aged 14 weeks). L) Change in tumour volume of BP tumours injected into CD-1 nude male mice and treated with vehicle, BRAF/MEKi, or BRAF/MEKi in combination with AR blockade (n = 10 mice/group, mice aged 11 weeks, BRAF/MEKi vs BRAF/MEKi + AR blockade p = 0.92). M) Change in tumour volume of BP tumours injected into CD-1 nude female mice and treated with vehicle, BRAF/MEKi, or BRAF/MEKi in combination with testosterone (n = 10 mice/group, mice aged 11 weeks, BRAF/MEKi vs BRAF/MEKi + testosterone p = 0.03). All tumour growth curves represent mean + SEM and were compared by ANOVA with multiple comparisons.

Extended Data Fig. 9 Effect of BRAF/MEKi on pERK, ZIP9/YAP1 associated transcripts and YAP1 associated transcripts in BP tumours.

A) Staining and quantification of phosphor-ERK in BP tumours of female and male mice on treated with vehicles, BRAF/MEK inhibition, or BRAF/MEK inhibition + testosterone. Histogram represent mean + SD. Differences were calculated using one-way ANOVA (n = 5/group). B) Staining and quantification of ZIP9/YAP1 associated transcripts in BP tumours of female and male mice on treatment with vehicles (n = 6), BRAF/MEK inhibition (n = 4), or BRAF/MEK inhibition + testosterone (n = 8). Histogram represent mean + SD. Differences were calculated using one-way ANOVA. C) Staining and quantification of YAP1 associated transcripts in BP tumours of female and male mice on treatment with vehicles (n = 6 females, 4 males) or BRAF/MEK inhibition (n = 4 females, 3 males. Histogram represent mean + SD. Differences were calculated using one-way ANOVA.

Extended Data Fig. 10 Non-targeting control line in BRAF/PTEN mice.

A) Volume of BP tumours established from the BP cell line engineered to express a non-targeting CRISPR control for male CD-1 nude mice treated with vehicle, BRAF/MEKi, or BRAF/MEKi in combination with AR blockade (n = 10 mice/group) B) Volume of BP tumours established from the BP cell line engineered to express a non-targeting CRISPR control for female CD-1 nude mice treated with vehicle, BRAF/MEKi, or BRAF/MEKi in combination with testosterone (n = 10 mice/group, p = 0.01).). All tumour growth curves represent mean + SEM and were compared by ANOVA with multiple comparisons.

Supplementary information

Reporting Summary

Supplementary Table 1

MD Anderson clinical cohort demographic and clinical data. Metadata for 51 patients who received neoadjuvant BRAF/MEKi targeted therapy for melanoma and 80 patients with metastatic melanoma who received definitive BRAF/MEKi targeted therapy. MPR+, major pathological response; MPR−, no major pathologic response; OR, odds ratio; ECOG, Eastern Cooperative Oncology Group; BMI, body mass index; LDH, lactate dehydrogenase. MPR is defined as less than 10% viable tumour on pathological examination. Clinical benefit is defined as stable disease for >6 months, partial response or complete response on the basis of RECIST 1.1 criteria.

Supplementary Table 2

Detailed clinical characteristics of MD Anderson metastatic melanoma cohort with RECIST 1.1 evaluable data. Metadata of patients with metastatic melanoma treated at MD Anderson included in the study with evaluable RECIST 1.1 data. IQR, interquartile range. Clinical benefit is defined as stable disease for >6 months, partial response or complete response on the basis of RECIST 1.1 criteria.

Supplementary Table 3

Percentage of AR-positive nuclei by sex, MPR and RECIST response. Percentage of AR-positive nuclei on the basis of immunofluorescence staining with associated sex, pathological responses and RECIST responses.

Supplementary Table 4

Differentially expressed genes of on-treatment clinical samples. Clinical samples underwent bulk RNA-seq with the following genes being differentially expressed in on-treatment samples that achieved a MPR to those that did not achieve a MPR.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Vellano, C.P., White, M.G., Andrews, M.C. et al. Androgen receptor blockade promotes response to BRAF/MEK-targeted therapy. Nature 606, 797–803 (2022). https://doi.org/10.1038/s41586-022-04833-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41586-022-04833-8

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research