Tumor mutation burden and circulating tumor DNA in combined CTLA-4 and PD-1 antibody therapy in metastatic melanoma - results of a prospective biomarker study

Andrea Forschner, Florian Battke, Dirk Hadaschik, Martin Schulze, Stephanie Weißgraeber, Chung-Ting Han, Maria Kopp, Maximilian Frick, Bernhard Klumpp, Nicola Tietze, Teresa Amaral, Peter Martus, Tobias Sinnberg, Thomas Eigentler, Ulrike Keim, Claus Garbe, Dennis Döcker, Saskia Biskup, Andrea Forschner, Florian Battke, Dirk Hadaschik, Martin Schulze, Stephanie Weißgraeber, Chung-Ting Han, Maria Kopp, Maximilian Frick, Bernhard Klumpp, Nicola Tietze, Teresa Amaral, Peter Martus, Tobias Sinnberg, Thomas Eigentler, Ulrike Keim, Claus Garbe, Dennis Döcker, Saskia Biskup

Abstract

Background: Metastasized or unresectable melanoma has been the first malignant tumor to be successfully treated with checkpoint inhibitors. Nevertheless, about 40-50% of the patients do not respond to these treatments and severe side effects are observed in up to 60%. Therefore, there is a high need to identify reliable biomarkers predicting response. Tumor Mutation Burden (TMB) is a debated predictor for response to checkpoint inhibitors and early measurement of ctDNA can help to detect treatment failure to immunotherapy in selected melanoma patients. However, it has not yet been clarified how TMB and ctDNA can be used to estimate response to combined CTLA-4 and PD-1 antibody therapy in metastatic melanoma.

Patients and methods: In this prospective biomarker study, we included 35 melanoma patients with ipilimumab (anti-CTLA-4) and nivolumab (anti-PD-1) therapy. In all patients, a tumor panel of 710 tumor-associated genes was applied (tumor vs. reference tissue comparison), followed by repetitive liquid biopsies. Cell-free DNA was extracted and at least one driver mutation was monitored. Treatment response was evaluated after about three months of therapy.

Results: TMB was significantly higher in responders than in nonresponders and TMB > 23.1 Mut/Mb (TMB-high) was associated with a survival benefit compared to TMB ≤ 23.1 Mut/Mb (TMB-low or TMB-intermediate). Furthermore, a > 50% decrease of cell-free DNA concentration or undetectable circulating tumor DNA (ctDNA), measured by tumor-specific variant copies/ml of plasma at first follow-up three weeks after treatment initiation were significantly associated with response to combined immunotherapy and improved overall survival, respectively. It is noticeable that no patient with TMB ≤ 23.1 Mut/Mb and detectable or increasing ctDNA at first follow-up responded to immunotherapy.

Conclusion: High TMB, > 50% decrease of cell-free DNA concentration, and undetectable ctDNA at first follow-up seem to be associated with response and overall survival under combined immunotherapy. The evaluation of ctDNA and cell-free DNA three weeks after treatment initiation may be suitable for early assessment of efficacy of immunotherapy.

Conflict of interest statement

AF served as consultant to Roche, Novartis, MSD, Pierre-Fabre; received travel support from Roche, Novartis, BMS, Pierre-Fabre, received speaker fees from Cegat, Roche, Novartis, BMS, MSD. TA received travel support from Novartis. TE served as consultant to Roche, Novartis, MSD, BMS, Pierre-Fabre; received speaker fees from Roche, Novartis, BMS, MSD. CG reports grants and personal fees from Novartis, BMS, Roche, personal fees from MSD. Personal fees from Amgen, Philogen, LEO, Incyte, outside the submitted work. DD received travel support from BMS. No competing interests were declared by the other authors.

Figures

Fig. 1
Fig. 1
a Comparison of tumor mutation burden (TMB) in responders and non-responders to combined immunotherapy. b Comparison of tumor mutation burden (TMB) in complete responders, partial responders, and non-responders to combined immunotherapy
Fig. 2
Fig. 2
a Cell-free DNA concentrations at start of combined immunotherapy (x axis) and at first follow-up (3–4 weeks later, y axis). Patients were classified into three groups, depending on the change in their cell-free DNA concentration as increasing (increase of more than 50%), decreasing (decrease of more than 50%), or stable. The respective thresholds are marked by broken lines. Increase of cell-free DNA is observed more frequently in non-responders. The four highest values can be seen in the inserted image in the upper right corner. b ctDNA, measured by tumor-specific variant copies/ml of plasma at start of combined immunotherapy (x axis), and at first follow-up (3–4 weeks later, y axis). Increase of ctDNA is almost only observed in progressive patients. Please note that multiple patients had undetectable ctDNA at both time points and are not visible in the plot as separate points due to overplotting (2 for complete response, 4 for partial response, 3 for progress)
Fig. 3
Fig. 3
Impact of baseline patients’ and disease characteristics on overall survival since the beginning of combined immunotherapy. 1Log rank test / 2Log rank test for Trend. *significant. a Tumor mutation burden (TMB) > 23.1 Mut/Mb vs. TMB ≤ 23.1 Mut/Mb, p = 0.061. b ctDNA measured by tumor-specific variant copies/ml of plasma detectable vs. undetectable at first follow-up, p = 0.006*1. c ctDNA measured by tumor-specific variant copies/ml of plasma increasing vs. not increasing at first follow-up, p = 0.03*1. d Cell-free DNA decrease > 50% vs. stable vs. increase > 50%, p = 0.005*2. e Targeted treatment (TT) before start of combined immunotherapy vs. no TT before, p = 0.001*1. f Men vs. women, p = 0.005*1. g Liver metastasis baseline vs. no liver metastasis baseline, p=0.013*1. h LDH baseline normal vs. elevated, p = 0.001*1
Fig. 4
Fig. 4
Impact of combined variables of TMB on overall survival since the beginning of combined immunotherapy. TMB > 23.1 Mut/Mb (TMB-H) TMB ≤ 23.1 Mut/Mb (TMB-L). Log rank test for Trend. *significant. a Tumor mutation burden (TMB) and ctDNA measured by tumor-specific variant copies/ml of plasma detectable vs. undetectable at first follow-up p = 0.005*. b Tumor mutation burden (TMB) and ctDNA measured by tumor-specific variant copies/ml of plasma increasing vs. not increasing, p = 0.032*. c Tumor mutation burden (TMB) and cell-free DNA decrease > 50% vs. stable vs. increase > 50% at first follow-up, p = 0.016*. d Tumor mutation burden (TMB) and presence of liver metastases, p = 0.018*. e Tumor mutation burden (TMB) and sex, p = 0.010*

References

    1. Ribas A, Hamid O, Daud A, Hodi FS, Wolchok JD, Kefford R, et al. Association of Pembrolizumab with Tumor Response and Survival among Patients with Advanced Melanoma. Jama. 2016;315(15):1600–1609. doi: 10.1001/jama.2016.4059.
    1. Robert C, Long GV, Brady B, Dutriaux C, Maio M, Mortier L, et al. Nivolumab in previously untreated melanoma without BRAF mutation. N Engl J Med. 2015;372(4):320–330. doi: 10.1056/NEJMoa1412082.
    1. Hodi FS, Chiarion-Sileni V, Gonzalez R, Grob JJ, Rutkowski P, Cowey CL, et al. Nivolumab plus ipilimumab or nivolumab alone versus ipilimumab alone in advanced melanoma (CheckMate 067): 4-year outcomes of a multicentre, randomised, phase 3 trial. Lancet Oncol. 2018;19(11):1480–1492. doi: 10.1016/S1470-2045(18)30700-9.
    1. Wolchok JD, Chiarion-Sileni V, Gonzalez R, Rutkowski P, Grob JJ, Cowey CL, et al. Overall Survival with Combined Nivolumab and Ipilimumab in Advanced Melanoma. N Engl J Med. 2017;377(14):1345–1356. doi: 10.1056/NEJMoa1709684.
    1. Larkin J, Chiarion-Sileni V, Gonzalez R, Grob JJ, Cowey CL, Lao CD, et al. Combined Nivolumab and Ipilimumab or monotherapy in untreated melanoma. N Engl J Med. 2015;373(1):23–34. doi: 10.1056/NEJMoa1504030.
    1. Puzanov I, Dummer R, Schachter J, Pavlick AC, Gonzalez R, Ascierto PA, et al. Efficacy based on tumor PD-L1 expression in KEYNOTE-002, a randomized comparison of pembrolizumab (pembro; MK-3475) versus chemotherapy in patients (pts) with ipilimumab-refractory (IPI-R) advanced melanoma (MEL) J Clin Oncol. 2015;33(15_suppl):3012. doi: 10.1200/jco.2015.33.15_suppl.3012.
    1. Weber JS, D'Angelo SP, Minor D, Hodi FS, Gutzmer R, Neyns B, et al. Nivolumab versus chemotherapy in patients with advanced melanoma who progressed after anti-CTLA-4 treatment (CheckMate 037): a randomised, controlled, open-label, phase 3 trial. Lancet Oncol. 2015;16(4):375–384. doi: 10.1016/S1470-2045(15)70076-8.
    1. Madore J, Vilain RE, Menzies AM, Kakavand H, Wilmott JS, Hyman J, et al. PD-L1 expression in melanoma shows marked heterogeneity within and between patients: implications for anti-PD-1/PD-L1 clinical trials. Pigment Cell Melanoma Res. 2015;28(3):245–253. doi: 10.1111/pcmr.12340.
    1. Tumeh PC, Hellmann MD, Hamid O, Tsai KK, Loo KL, Gubens MA, et al. Liver metastasis and treatment outcome with anti-PD-1 monoclonal antibody in patients with melanoma and NSCLC. Cancer Immunol Res. 2017;5(5):417–424. doi: 10.1158/2326-6066.CIR-16-0325.
    1. Tumeh PC, Harview CL, Yearley JH, Shintaku IP, Taylor EJ, Robert L, et al. PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature. 2014;515(7528):568–571. doi: 10.1038/nature13954.
    1. Alexandrov LB, Nik-Zainal S, Wedge DC, Aparicio SA, Behjati S, Biankin AV, et al. Signatures of mutational processes in human cancer. Nature. 2013;500(7463):415–421. doi: 10.1038/nature12477.
    1. Van Allen EM, Miao D, Schilling B, Shukla SA, Blank C, Zimmer L, et al. Genomic correlates of response to CTLA-4 blockade in metastatic melanoma. Science. 2015;350(6257):207–211. doi: 10.1126/science.aad0095.
    1. Snyder A, Makarov V, Merghoub T, Yuan J, Zaretsky JM, Desrichard A, et al. Genetic basis for clinical response to CTLA-4 blockade in melanoma. N Engl J Med. 2014;371(23):2189–2199. doi: 10.1056/NEJMoa1406498.
    1. Johnson DB, Frampton GM, Rioth MJ, Yusko E, Xu Y, Guo X, et al. Targeted next generation sequencing identifies markers of response to PD-1 blockade. Cancer Immunol Res. 2016;4(11):959–967. doi: 10.1158/2326-6066.CIR-16-0143.
    1. Goodman AM, Kato S, Bazhenova L, Patel SP, Frampton GM, Miller V, et al. Tumor mutational burden as an independent predictor of response to immunotherapy in diverse cancers. Mol Cancer Ther. 2017;16(11):2598–2608. doi: 10.1158/1535-7163.MCT-17-0386.
    1. Hugo W, Zaretsky JM, Sun L, Song C, Moreno BH, Hu-Lieskovan S, et al. Genomic and transcriptomic features of response to anti-PD-1 therapy in metastatic melanoma. Cell. 2016;165(1):35–44. doi: 10.1016/j.cell.2016.02.065.
    1. Shin DS, Zaretsky JM, Escuin-Ordinas H, Garcia-Diaz A, Hu-Lieskovan S, Kalbasi A, et al. Primary resistance to PD-1 blockade mediated by JAK1/2 mutations. Cancer Discov. 2017;7(2):188–201. doi: 10.1158/-16-1223.
    1. Roh W, Chen PL, Reuben A, Spencer CN, Prieto PA, Miller JP, et al. Integrated molecular analysis of tumor biopsies on sequential CTLA-4 and PD-1 blockade reveals markers of response and resistance. Sci Transl Med. 2017;9(379):eaah3560. doi: 10.1126/scitranslmed.aah3560.
    1. Samstein RM, Lee CH, Shoushtari AN, Hellmann MD, Shen R, Janjigian YY, et al. Tumor mutational load predicts survival after immunotherapy across multiple cancer types. Nat Genet. 2019;51(2):202–206. doi: 10.1038/s41588-018-0312-8.
    1. Lee JH, Long GV, Boyd S, Lo S, Menzies AM, Tembe V, et al. Circulating tumour DNA predicts response to anti-PD1 antibodies in metastatic melanoma. Ann Oncol. 2017;28(5):1130–1136. doi: 10.1093/annonc/mdx026.
    1. Rowe SP, Luber B, Makell M, Brothers P, Santmyer J, Schollenberger MD, et al. From validity to clinical utility: the influence of circulating tumor DNA on melanoma patient management in a real-world setting. Mol Oncol. 2018;12(10):1661–1672. doi: 10.1002/1878-0261.12373.
    1. Herbreteau G, Vallee A, Knol AC, Theoleyre S, Quereux G, Varey E, et al. Quantitative monitoring of circulating tumor DNA predicts response of cutaneous metastatic melanoma to anti-PD1 immunotherapy. Oncotarget. 2018;9(38):25265–25276. doi: 10.18632/oncotarget.25404.
    1. Buchhalter I, Rempel E, Endris V, Allgauer M, Neumann O, Volckmar AL, et al. Size matters: dissecting key parameters for panel-based tumor mutational burden (TMB) analysis. Int J Cancer. 2018;144(4):848–858. doi: 10.1002/ijc.31878.
    1. Eisenhauer EA, Therasse P, Bogaerts J, Schwartz LH, Sargent D, Ford R, et al. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1) Eur Journal Cancer. 2009;45(2):228–247. doi: 10.1016/j.ejca.2008.10.026.
    1. Kent WJ, Sugnet CW, Furey TS, Roskin KM, Pringle TH, Zahler AM, et al. The human genome browser at UCSC. Genome Res. 2002;12(6):996–1006. doi: 10.1101/gr.229102.
    1. Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W, et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 1997;25(17):3389–3402. doi: 10.1093/nar/25.17.3389.
    1. Whale AS, Devonshire AS, Karlin-Neumann G, Regan J, Javier L, Cowen S, et al. International Interlaboratory digital PCR study demonstrating high reproducibility for the measurement of a rare sequence variant. Anal Chem. 2017;89(3):1724–1733. doi: 10.1021/acs.analchem.6b03980.
    1. Huggett JF, Foy CA, Benes V, Emslie K, Garson JA, Haynes R, et al. The digital MIQE guidelines: minimum information for publication of quantitative digital PCR experiments. Clin Chem. 2013;59(6):892–902. doi: 10.1373/clinchem.2013.206375.
    1. Chalmers ZR, Connelly CF, Fabrizio D, Gay L, Ali SM, Ennis R, et al. Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden. Genome Med. 2017;9(1):34. doi: 10.1186/s13073-017-0424-2.
    1. Spindler KL, Appelt AL, Pallisgaard N, Andersen RF, Brandslund I, Jakobsen A. Cell-free DNA in healthy individuals, noncancerous disease and strong prognostic value in colorectal cancer. Int J Cancer. 2014;135(12):2984–2991. doi: 10.1002/ijc.28946.
    1. Lyu GY, Yeh YH, Yeh YC, Wang YC. Mutation load estimation model as a predictor of the response to cancer immunotherapy. NPJ Genom Med. 2018;3:12. doi: 10.1038/s41525-018-0051-x.
    1. Ashida A, Sakaizawa K, Uhara H, Okuyama R. Circulating tumour DNA for monitoring treatment response to anti-PD-1 immunotherapy in melanoma patients. Acta Derm Venereol. 2017;97(10):1212–1218. doi: 10.2340/00015555-2748.
    1. Zehir A, Benayed R, Shah RH, Syed A, Middha S, Kim HR, et al. Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients. Nat Med. 2017;23(6):703–713. doi: 10.1038/nm.4333.
    1. Hayward NK, Wilmott JS, Waddell N, Johansson PA, Field MA, Nones K, et al. Whole-genome landscapes of major melanoma subtypes. Nature. 2017;545(7653):175–180. doi: 10.1038/nature22071.
    1. Furney SJ, Turajlic S, Stamp G, Thomas JM, Hayes A, Strauss D, et al. The mutational burden of acral melanoma revealed by whole-genome sequencing and comparative analysis. Pigment Cell Melanoma Res. 2014;27(5):835–838. doi: 10.1111/pcmr.12279.
    1. Furney SJ, Turajlic S, Stamp G, Nohadani M, Carlisle A, Thomas JM, et al. Genome sequencing of mucosal melanomas reveals that they are driven by distinct mechanisms from cutaneous melanoma. J Pathol. 2013;230(3):261–269. doi: 10.1002/path.4204.
    1. Matsushita H, Vesely MD, Koboldt DC, Rickert CG, Uppaluri R, Magrini VJ, et al. Cancer exome analysis reveals a T-cell-dependent mechanism of cancer immunoediting. Nature. 2012;482(7385):400–404. doi: 10.1038/nature10755.
    1. Riaz N, Morris L, Havel JJ, Makarov V, Desrichard A, Chan TA. The role of neoantigens in response to immune checkpoint blockade. Int Immunol. 2016;28(8):411–419. doi: 10.1093/intimm/dxw019.
    1. Hellmann MD, Callahan MK, Awad MM, Calvo E, Ascierto PA, Atmaca A, et al. Tumor mutational burden and efficacy of Nivolumab monotherapy and in combination with Ipilimumab in small-cell lung Cancer. Cancer Cell. 2018;33(5):853–861. doi: 10.1016/j.ccell.2018.04.001.
    1. Morrison C, Pabla S, Conroy JM, Nesline MK, Glenn ST, Dressman D, et al. Predicting response to checkpoint inhibitors in melanoma beyond PD-L1 and mutational burden. J Immunother Cancer. 2018;6(1):32. doi: 10.1186/s40425-018-0344-8.
    1. Gangadhar TC, Savitch SL, Yee SS, Xu W, Huang AC, Harmon S, et al. Feasibility of monitoring advanced melanoma patients using cell-free DNA from plasma. Pigment Cell Melanoma Res. 2018;31(1):73–81. doi: 10.1111/pcmr.12623.
    1. Seith F, Forschner A, Schmidt H, Pfannenberg C, Guckel B, Nikolaou K, et al. 18F-FDG-PET detects complete response to PD1-therapy in melanoma patients two weeks after therapy start. Eur J Nucl Med Mol Imaging. 2018;45(1):95–101. doi: 10.1007/s00259-017-3813-2.
    1. Conforti F, Pala L, Bagnardi V, De Pas T, Martinetti M, Viale G, et al. Cancer immunotherapy efficacy and patients' sex: a systematic review and meta-analysis. Lancet Oncol. 2018;19(6):737–746. doi: 10.1016/S1470-2045(18)30261-4.
    1. Gupta S, Artomov M, Goggins W, Daly M, Tsao H. Gender disparity and mutation burden in metastatic melanoma. J Natl Cancer Inst. 2015;107(11):djv221. doi: 10.1093/jnci/djv221.
    1. Xiao D, Pan H, Li F, Wu K, Zhang X, He J. Analysis of ultra-deep targeted sequencing reveals mutation burden is associated with gender and clinical outcome in lung adenocarcinoma. Oncotarget. 2016;7(16):22857–22864. doi: 10.18632/oncotarget.8213.
    1. Nosrati A, Tsai KK, Goldinger SM, Tumeh P, Grimes B, Loo K, et al. Evaluation of clinicopathological factors in PD-1 response: derivation and validation of a prediction scale for response to PD-1 monotherapy. Br J Cancer. 2017;116(9):1141–1147. doi: 10.1038/bjc.2017.70.
    1. Joseph RW, Elassaiss-Schaap J, Kefford R, Hwu WJ, Wolchok JD, Joshua AM, et al. Baseline tumor size is an independent prognostic factor for overall survival in patients with melanoma treated with Pembrolizumab. Clin Cancer Res. 2018;24(20):4960–4967.
    1. Shi W, Ng CKY, Lim RS, Jiang T, Kumar S, Li X, et al. Reliability of whole-exome sequencing for assessing Intratumor genetic heterogeneity. Cell Rep. 2018;25(6):1446–1457. doi: 10.1016/j.celrep.2018.10.046.
    1. Lee JHJ, Lyle M, Menzies AM, Chan MMK, Lo S, Clements A, et al. Metastasis-specific patterns of response and progression with anti-PD-1 treatment in metastatic melanoma. Pigment Cell Melanoma Res. 2018;31(3):404–410. doi: 10.1111/pcmr.12675.

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