Abstract
Neoadjuvant ipilimumab plus nivolumab showed high pathologic response rates (pRRs) in patients with macroscopic stage III melanoma in the phase 1b OpACIN (NCT02437279) and phase 2 OpACIN-neo (NCT02977052) studies1,2. While the results are promising, data on the durability of these pathologic responses and baseline biomarkers for response and survival were lacking. After a median follow-up of 4 years, none of the patients with a pathologic response (n = 7/9 patients) in the OpACIN study had relapsed. In OpACIN-neo (n = 86), the 2-year estimated relapse-free survival was 84% for all patients, 97% for patients achieving a pathologic response and 36% for nonresponders (P < 0.001). High tumor mutational burden (TMB) and high interferon-gamma-related gene expression signature score (IFN-γ score) were associated with pathologic response and low risk of relapse; pRR was 100% in patients with high IFN-γ score/high TMB; patients with high IFN-γ score/low TMB or low IFN-γ score/high TMB had pRRs of 91% and 88%; while patients with low IFN-γ score/low TMB had a pRR of only 39%. These data demonstrate long-term benefit in patients with a pathologic response and show the predictive potential of TMB and IFN-γ score. Our findings provide a strong rationale for a randomized phase 3 study comparing neoadjuvant ipilimumab plus nivolumab versus standard adjuvant therapy with antibodies against the programmed cell death protein-1 (anti-PD-1) in macroscopic stage III melanoma.
This is a preview of subscription content, access via your institution
Relevant articles
Open Access articles citing this article.
-
Combination of immune-checkpoint inhibitors and targeted therapies for melanoma therapy: The more, the better?
Cancer and Metastasis Reviews Open Access 06 April 2023
-
A novel cuproptosis-related gene model predicts outcomes and treatment responses in pancreatic adenocarcinoma
BMC Cancer Open Access 10 March 2023
-
PI3K pathway mutation predicts an activated immune microenvironment and better immunotherapeutic efficacy in head and neck squamous cell carcinoma
World Journal of Surgical Oncology Open Access 02 March 2023
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$189.00 per year
only $15.75 per issue
Rent or buy this article
Get just this article for as long as you need it
$39.95
Prices may be subject to local taxes which are calculated during checkout
Data availability
DNA-sequencing and RNA-sequencing data generated during the study will be deposited in the European Genome-phenome Archive (EGA) under the accession codes EGAS00001004832 (DNA) and EGAS00001004833 (RNA), and will be made available on reasonable request. Data requests will be reviewed by the institutional review board of the Netherlands Cancer Institute and applying researchers will need to sign a data access agreement after approval.
References
Blank, C. U. et al. Neoadjuvant versus adjuvant ipilimumab plus nivolumab in macroscopic stage III melanoma. Nat. Med. 24, 1655–1661 (2018).
Rozeman, E. A. et al. Identification of the optimal combination dosing schedule of neoadjuvant ipilimumab plus nivolumab in macroscopic stage III melanoma (OpACIN-neo): a multicentre, phase 2, randomised, controlled trial. Lancet Oncol. 20, 948–960 (2019).
Hauschild, A. et al. Longer follow-up confirms relapse-free survival benefit with adjuvant dabrafenib plus trametinib in patients with resected BRAFV600-mutant stage III melanoma. J. Clin. Oncol. https://doi.org/10.1200/JCO.18.01219 (2018).
Eggermont, A. M. M. et al. Longer follow-up confirms recurrence-free survival benefit of adjuvant pembrolizumab in high-risk stage III Melanoma: updated results from the EORTC 1325-MG/KEYNOTE-054 Trial. J. Clin. Oncol. https://doi.org/10.1200/jco.20.02110 (2020).
Ascierto, P. A. et al. Adjuvant nivolumab versus ipilimumab in resected stage IIIB-C and stage IV melanoma (CheckMate 238): 4-year results from a multicentre, double-blind, randomised, controlled, phase 3 trial. Lancet Oncol. https://doi.org/10.1016/s1470-2045(20)30494-0 (2020).
Bloemendal, M. et al. Early recurrence in completely resected IIIB and IIIC melanoma warrants restaging prior to adjuvant therapy. Ann. Surg. Oncol. 26, 3945–3952 (2019).
Weber, J. et al. Adjuvant nivolumab versus ipilimumab in resected stage III or IV melanoma. N. Engl. J. Med. 377, 1824–1835 (2017).
Liu, J. et al. Improved efficacy of neoadjuvant compared to adjuvant immunotherapy to eradicate metastatic disease. Cancer Discov. 6, 1382–1399 (2016).
Brockwell, N. K. et al. Neoadjuvant interferons: critical for effective PD-1–based immunotherapy in TNBC. Cancer Immunol. Res. 5, 871–884 (2017).
Bourgeois-Daigneault, M.-C. et al. Neoadjuvant oncolytic virotherapy before surgery sensitizes triple-negative breast cancer to immune checkpoint therapy. Sci. Transl. Med. 10, eaao1641 (2018).
Brooks, J. et al. Perioperative, spatiotemporally coordinated activation of T and NK cells prevents recurrence of pancreatic cancer. Cancer Res. 78, 475–488 (2018).
O’Donnell, J. S., Hoefsmit, E. P., Smyth, M. J., Blank, C. U. & Teng, M. W. The promise of neoadjuvant immunotherapy and surgery for cancer treatment. Clin. Cancer Res. 25, 5743–5751 (2019).
Amaria, R. N. et al. Neoadjuvant immune checkpoint blockade in high-risk resectable melanoma. Nat. Med. 24, 1649–1654 (2018).
Ayers, M. et al. IFN-γ-related mRNA profile predicts clinical response to PD-1 blockade. J. Clin. Invest. 127, 2930–2940 (2017).
Becht, E. et al. Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression. Genome Biol. 17, 1–20 (2016).
Menzies, A. M. et al. Pathological response and survival neoadjuvant therapy in melanoma: a pooled analysis from the Neoadjuvant Melanoma Consortium. J. Clin. Oncol. 37, 9503 (2019).
Dummer, R. et al. Adjuvant dabrafenib plus trametinib versus placebo in patients with resected, BRAFV600-mutant, stage III melanoma (COMBI-AD): exploratory biomarker analyses from a randomised, phase 3 trial. Lancet Oncol. 21, 358–372 (2020).
Lu, S. et al. Comparison of biomarker modalities for predicting response to PD-1/PD-L1 checkpoint blockade: a systematic review and meta-analysis. JAMA Oncol. 5, 1195–1204 (2019).
Chae, Y. K. et al. Clinical implications of circulating tumor DNA tumor mutational burden (ctDNA TMB) in non-small cell lung cancer. Oncologist 24, 820–828 (2019).
Lee, J. H. et al. Pre-operative ctDNA predicts survival in high-risk stage III cutaneous melanoma patients. Ann. Oncol. 30, 815–822 (2019).
Rowe, S. P. et al. From validity to clinical utility: the influence of circulating tumor DNA on melanoma patient management in a real-world setting. Mol. Oncol. 12, 1661–1672 (2018).
Fancello, L., Gandini, S., Pelicci, P. G. & Mazzarella, L. Tumor mutational burden quantification from targeted gene panels: major advancements and challenges. J. Immunother. Cancer 7, 183 (2019).
Yuan, J. et al. Pretreatment serum VEGF is associated with clinical response and overall survival in advanced melanoma patients treated with ipilimumab. Cancer Immunol. Res. 2, 127–132 (2014).
Ott, P. A., Hodi, F. S. & Buchbinder, E. I. Inhibition of immune checkpoints and vascular endothelial growth factor as combination therapy for metastatic melanoma: an overview of rationale, preclinical evidence and initial clinical data. Front. Oncol. 5, 202 (2015).
Chen, P.-L. et al. Analysis of immune signatures in longitudinal tumor samples yields insight into biomarkers of response and mechanisms of resistance to immune checkpoint blockade. Cancer Discov. 6, 827–837 (2016).
Voron, T. et al. VEGF-A modulates expression of inhibitory checkpoints on CD8+ T cells in tumors. J. Exp. Med. 212, 139–148 (2015).
Hodi, F. S. 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. 19, 1480–1492 (2018).
Eggermont, A. M. M. et al. Adjuvant pembrolizumab versus placebo in resected stage III melanoma. N. Engl. J. Med. 378, 1789–1801 (2018).
Eggermont, A. M. M. et al. Association between immune-related adverse events and recurrence-free survival among patients with stage III melanoma randomized to receive pembrolizumab or placebo: a secondary analysis of a randomized clinical trial. JAMA Oncol. https://doi.org/10.1001/jamaoncol.2019.5570 (2020).
Mandalà, M. et al. An analysis of nivolumab-mediated adverse events and association with clinical efficacy in resected stage III or IV melanoma (CheckMate 238). J. Clin. Oncol. 37, 9584–9584 (2019).
Blank, C. U. et al. First safety and efficacy results of PRADO: A phase II study of personalized response-driven surgery and adjuvant therapy after neoadjuvant ipilimumab and nivolumab in resectable stage III melanoma. J. Clin. Oncol. 38, 10002–10002 (2020).
Tetzlaff, M. T. et al. Pathological assessment of resection specimens after neoadjuvant therapy for metastatic melanoma. Ann. Oncol. 29, 1861–1868 (2018).
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
Anders, S., Pyl, P. T. & Huber, W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).
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).
Liberzon, A. et al. The molecular signatures database hallmark gene set collection. Cell Syst. 1, 417–425 (2015).
Subramanian, A. et al. Gene-set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).
Van der Auwera, G. A. et al. From FASTQ data to high-confidence variant calls: the genome analysis toolkit best practices pipeline. Curr. Protoc. Bioinformatics 43, 11.10.1–11.10. 33 (2013).
Acknowledgements
We thank the patients and their families for participating in the study. We thank all investigators and members of the clinical trial teams in Melanoma Institute Australia, the Netherlands Cancer Institute and the Karolinska Institutet; the Netherlands Cancer Institute-Antoni van Leeuwenhoek Core Facility Molecular Pathology & Biobanking for supplying biobank material and/or laboratory support; the Genomics Core Facility for their support regarding sequencing; and S. Vanhoutvin for financial management. We acknowledge A. Evans and B. Stegenga from Bristol Myers Squibb for scientific input and support. G.V.L. is supported by an Australian National Health and Medical Research Council (NHMRC) Practitioner Fellowship and the Medical Foundation at the University of Sydney. A.M.M. is supported by Cancer Institute New South Wales fellowship and Melanoma Institute Australia. R.A.S. is supported by NHMRC Practitioner Fellowship. Support from an Australian NHMRC program grant (to G.V.L. and R.A.S.), the Ainsworth Foundation, the Fairfax Foundation and the Cameron family is also gratefully acknowledged. The authors also acknowledge assistance from colleagues at their various institutions.
Author information
Authors and Affiliations
Contributions
C.U.B. designed the study and wrote the study protocol. A.C.J.v.A. and T.N.M.S. provided additional input to the study design. E.A.R. coordinated the trial, analyzed and interpreted clinical and translational data and wrote the first draft of the manuscript with E.P.H., I.L.M.R. and J.M.V. C.U.B. co-wrote the manuscript. E.A.R., R.P.M.S., H.E., K. Shannon, J.B.A.G.H., J.S., S. Ch’ng, O.E.N., H.A.M., S.A., W.M.C.K., C.L.Z., W.J.v.H., A.J.S., A.C.J.v.A., A.M.M., G.V.L. and C.U.B. recruited and treated patients and collected data. B.A.v.d.W. and R.A.S. reviewed and scored the pathology of all cases, including grading pathologic responses. P.D. and O.K. (under the supervision of D.S.P.) performed the bioinformatics analysis. K. Sikorska performed the statistical analysis. A.T.A. and L.G.G.-O. were responsible for central and local data management. M.G. was a clinical project manager involved in the trial. E.P.H. performed the analysis of the plasma proteomics data. R.M.K. was responsible for the sequencing. S. Cornelissen performed DNA and RNA isolations. A.B. coordinated and contributed to translational laboratory logistics and immunohistochemistry and molecular laboratory work. Every author contributed to the initial draft of the manuscript and agreed on submission for publication. All authors interpreted the data, reviewed the manuscript and approved the final version.
Corresponding author
Ethics declarations
Competing interests
E.P.H., I.L.M.R., J.M.V., O.K., P.D., K. Sikorska, H.E., M.G., A.T.A., L.G.G.-O., K. Shannon, J.S., S. Ch’ng, O.E.N., H.A.M., S.A., R.M.K., S. Cornelissen, A.B., W.M.C.K., C.L.Z., W.J.v.H. and A.J.S. declared no competing interests. E.A.R. reports travel support from NanoString Technologies and MSD. R.P.M.S. has served on advisory boards for MSD Novartis Qbiotics and received honoraria from BMS. B.A.v.d.W. has served on advisory boards for BMS. J.B.A.G.H. has served on advisory boards for AIMM, Achilles, AZ/MEDimmune, Amgen, Bayer, BMS, GSK, Ipsen, Immunocore, MSD, Merck Sorono, Neon Therapeutics, Neogene Therapeutics, Novartis, Pfizer, Roche/Genentech, Sanofi, Seattle Genetics, Third Rock Ventures and Vaximm, and reports research fees paid to the institute from BMS, MSD, Novartis and Neon Therapeutics. D.S.P. is cofounder, shareholder and advisor of Immagene BV. A.C.J.v.A. has served on advisory boards for Amgen, BMS, Novartis, MSD-Merck, Merck-Pfizer, Sanofi and 4SC, and reports research fees paid to the institute from Amgen, BMS and Merck-Pfizer. R.A.S. reports financial support from Qbiotics, Novartis, NeraCare, AMGEN, BMS, Myriad Genetics, GlaxoSmithKline and Merck Sharp & Dohme. T.N.M.S. has served on advisory boards for Adaptive Biotechnologies, AIMM Therapeutics, Allogene Therapeutics, Merus, BioNTech, Scenic Biotech; reports financial support from Merck KGaA; and is stockholder in AIMM Therapeutics, Allogene Therapeutics, Merus, Neogene Therapeutics, BioNTech and Scenic Biotech. A.M.M. has served on advisory boards for BMS, MSD, Novartis, Roche, Pierre Fabre and QBiotics. G.V.L. has served on advisory boards for Aduro, Amgen, BMS, Highlight Therapeutics S.L., Mass-Array, Merck, MSD, Novartis, OncoSec Medical, Pierre Fabre, Roche, Qbiotics and Sandoz. C.U.B. has served on advisory boards for BMS, MSD, Roche, Novartis, GSK, AZ, Pfizer, Lilly, GenMab, Pierre Fabre and Third Rock Ventures, for which the institute received funding; received research funding from BMS, Novartis and NanoString, all paid to the institute; stock ownership: Uniti Cars; and is cofounder of Immagene BV.
Additional information
Peer review information Javier Carmona was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data
Extended Data Fig. 1 Study design OpACIN, event-free survival and overall survival of OpACIN.
a, Study design of the OpACIN study. Patients were randomized to receive 4 cycles of ipilimumab 3 mg/kg + nivolumab 1 mg/kg every 3 weeks after surgery (adjuvant arm, n = 10) or 2 cycles of ipilimumab 3 mg/kg + nivolumab 1 mg/kg every 3 weeks followed by surgery and thereafter again 2 cycles of ipilimumab 3 mg/kg + nivolumab 1 mg/kg (neoadjuvant arm, n = 10). A biopsy was taken at screening and blood samples were taken at screening, baseline, week 6, week 12 and week 18. IPI; ipilimumab, NIVO; nivolumab, PBMC; peripheral blood mononuclear cells. b, Event-free survival by treatment arm and c, Overall survival by treatment arm of the OpACIN study. Kaplan-Meier curves were generated including all patients from the adjuvant arm (red, n = 10) and neoadjuvant arm (blue, n = 10).
Extended Data Fig. 2 Study design and flowchart OpACIN-neo.
a, Study design of the OpACIN-neo study. Patients were randomized to receive 2 cycles of ipilimumab 3 mg/kg + nivolumab 1 mg/kg every 3 weeks (arm A, n = 30), 2 cycles of ipilimumab 1 mg/kg + nivolumab 3 mg/kg every 3 weeks (arm B, n = 30) or 2 cycles of ipilimumab 3 mg/kg every 3 weeks directly followed by 2 cycles nivolumab 3 mg/kg every 2 weeks (arm C, n = 26). Surgery was planned after 6 weeks. A biopsy was taken at screening and blood samples were taken at screening, baseline, week 6 and week 12. IPI; ipilimumab, NIVO; nivolumab, PBMC; peripheral blood mononuclear cells. b, Flowchart of the OpACIN-neo study showing the number of patients screened, allocated to a treatment arm, starting immunotherapy and undergoing surgery per treatment arm.
Extended Data Fig. 3 Event-free survival and overall survival of OpACIN-neo.
a, EFS for the total population of the OpACIN-neo study. A Kaplan-Meier curve for EFS of all patients (n = 86) was generated. The corresponding 95% CI is displayed and was computed using log transformation. b, EFS of the OpACIN-neo study by treatment arm including all patients from arm A (blue, n = 30), arm B (orange, n = 30) and arm C (purple, n = 26). P values were calculated using the log-rank test (two-sided). c, OS for the total population of the OpACIN-neo study. A Kaplan-Meier curve for OS of all patients (n = 86) was generated. d, OS of the OpACIN-neo study by treatment arm including all patients from arm A (blue, n = 30), arm B (orange, n = 30) and arm C (purple, n = 26). a-b, The asterisk denotes the patient who died due to irAEs.
Extended Data Fig. 4 Ongoing surgery-related adverse events of OpACIN-neo.
Frequency of maximum grade and ongoing surgery-related adverse events (AEs) of the OpACIN-neo study. Frequencies of maximum grade AEs are displayed in light blue (grade 1–2) and dark blue (grade 3–5), and frequencies of ongoing AEs in orange (grade 1–2) and red (grade 3–5). AEs that were reported at a frequency of >5% and all grade 3–5 AEs were included. All patients (n = 86) were included in the analysis of maximum grade AEs; for ongoing AEs only patients alive at time of data cutoff (n = 81) were included.
Extended Data Fig. 5 Pathologic response rates according to subgroups.
Forest plot of data for all patients who underwent surgery (n = 85). pRRs according to demographic, clinical and tumor characteristics are displayed. The 95% CIs were calculated using the Clopper-Pearson method. PD-L1 expression on pretreatment tumor biopsies was assessed centrally with an automated lab-validated immunohistochemistry assay, using the 22C3 antibody on a Ventana platform. PD-L1 expression was determined by the Tumor Proportion Score (TPS; the percentage of tumor cells with complete or partial membranous staining at any intensity).
Extended Data Fig. 6 Whole-exome sequencing and RNA sequencing analysis of pretreatment tumor biopsies.
a, Mutational load and mutational patterns of recurrent mutated cutaneous melanoma genes found by whole-exome sequencing. The frequency, mutation type and base changes are indicated. Each column represents one patient (n = 60 patients). b, Correlation between the IFN-γ score (values displayed as the average z-score of all the genes within the IFN-γ signature14) and TMB (displayed in log scale) for patients with pathologic response (n = 42, green) and no pathologic response (n = 17, red). The correlation coefficient and P value were calculated using the Pearson’s correlation method. c, TMB and baseline average expression of IFN-γ score of patients with a response (green dots) and patients without a response (red dots). The quadrants are determined by the optimal cutoff for each of the biomarkers as defined by the sROC curves. Each quadrant indicates the number of responding patients and total number of patients in the corresponding quadrant. Data were available for 59 patients. d, sROC curve showing the AUC for the combination of the IFN-γ score and TMB (0.83) (n = 59). e, Heatmap of the MCP counter15 RNA gene signature ordered according to average signature expression per response category of baseline tumor biopsies (n = 65). The MCP counter signature expresses the abundance of eight immune and two stromal cell populations. Each cell type is represented by the averaged z-score of the genes that it is consisted of, which were previously normalized by DESeq2. The score was computed from the average expression of all the ten cell types that form the MCP counter signature. Columns represent patients (green: pathological response/no relapse; red: no pathological response/relapse; grey: not evaluable (NE); blue: treatment arm A; orange: treatment arm B; purple: treatment arm C) and rows represent genes. Positive values (red) indicate higher expression and negative (blue) indicate lower expression. f, Gene set enrichment analysis displaying hallmark gene sets that are significantly enriched in responders (green) or nonresponders (red). Pathways are ordered according to the FDR. FDRs were computed as previously described37.
Extended Data Fig. 7 Extended plasma analysis using Olink proteomic assay.
a, PDCD1, CXCL9, CXCL10 normalized protein expression (NPX) in plasma of patients (n = 85) before treatment (pretreatment; round dots) and after treatment (post-treatment; triangle dots) measured with Olink immunoassay (green: patients with pathologic response, n = 64; red: patients without pathologic response, n = 21). Data for PDCD1 are missing for pretreatment samples of 9 patients (all responders) because values were below detection limit. The mean and SD are shown. P values were calculated using the paired Student’s t-test (two-sided). b, Heatmap of VEGFR2, CX3CL1 and PD-L2 NPX in plasma of patients before start of treatment. The heatmap depicts the ordered mean expression of these three genes of the 86 patients included in the OpACIN-neo cohort. The score of each patient expresses the baseline averaged z-score of the three mentioned genes mentioned beforehand which was previously normalized by DESeq2. Each column represents a different patient (green: response/no relapse; red: no response/relapse; blue: arm A; orange: arm B; purple: arm C) and rows indicate protein expression. Positive values (red) indicate higher expression and negative values (blue) indicate lower expression.
Supplementary information
Supplementary Information
Supplementary Tables 1–9 and study protocols.
Rights and permissions
About this article
Cite this article
Rozeman, E.A., Hoefsmit, E.P., Reijers, I.L.M. et al. Survival and biomarker analyses from the OpACIN-neo and OpACIN neoadjuvant immunotherapy trials in stage III melanoma. Nat Med 27, 256–263 (2021). https://doi.org/10.1038/s41591-020-01211-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41591-020-01211-7
This article is cited by
-
Emerging precision neoadjuvant systemic therapy for patients with resectable non-small cell lung cancer: current status and perspectives
Biomarker Research (2023)
-
A novel cuproptosis-related gene model predicts outcomes and treatment responses in pancreatic adenocarcinoma
BMC Cancer (2023)
-
PI3K pathway mutation predicts an activated immune microenvironment and better immunotherapeutic efficacy in head and neck squamous cell carcinoma
World Journal of Surgical Oncology (2023)
-
Immune-based combination therapy to convert immunologically cold tumors into hot tumors: an update and new insights
Acta Pharmacologica Sinica (2023)
-
Personalizing neoadjuvant immune-checkpoint inhibition in patients with melanoma
Nature Reviews Clinical Oncology (2023)
