Pilot study of bempegaldesleukin in combination with nivolumab in patients with metastatic sarcoma

Sandra P D'Angelo, Allison L Richards, Anthony P Conley, Hyung Jun Woo, Mark A Dickson, Mrinal Gounder, Ciara Kelly, Mary Louise Keohan, Sujana Movva, Katherine Thornton, Evan Rosenbaum, Ping Chi, Benjamin Nacev, Jason E Chan, Emily K Slotkin, Hannah Kiesler, Travis Adamson, Lilan Ling, Pavitra Rao, Shreyaskumar Patel, Jonathan A Livingston, Samuel Singer, Narasimhan P Agaram, Cristina R Antonescu, Andrew Koff, Joseph P Erinjeri, Sinchun Hwang, Li-Xuan Qin, Mark T A Donoghue, William D Tap, Sandra P D'Angelo, Allison L Richards, Anthony P Conley, Hyung Jun Woo, Mark A Dickson, Mrinal Gounder, Ciara Kelly, Mary Louise Keohan, Sujana Movva, Katherine Thornton, Evan Rosenbaum, Ping Chi, Benjamin Nacev, Jason E Chan, Emily K Slotkin, Hannah Kiesler, Travis Adamson, Lilan Ling, Pavitra Rao, Shreyaskumar Patel, Jonathan A Livingston, Samuel Singer, Narasimhan P Agaram, Cristina R Antonescu, Andrew Koff, Joseph P Erinjeri, Sinchun Hwang, Li-Xuan Qin, Mark T A Donoghue, William D Tap

Abstract

PD-1 blockade (nivolumab) efficacy remains modest for metastatic sarcoma. In this paper, we present an open-label, non-randomized, non-comparative pilot study of bempegaldesleukin, a CD122-preferential interleukin-2 pathway agonist, with nivolumab in refractory sarcoma at Memorial Sloan Kettering/MD Anderson Cancer Centers (NCT03282344). We report on the primary outcome of objective response rate (ORR) and secondary endpoints of toxicity, clinical benefit, progression-free survival, overall survival, and durations of response/treatment. In 84 patients in 9 histotype cohorts, all patients experienced ≥1 adverse event and treatment-related adverse event; 1 death was possibly treatment-related. ORR was highest in angiosarcoma (3/8) and undifferentiated pleomorphic sarcoma (2/10), meeting predefined endpoints. Results of our exploratory investigation of predictive biomarkers show: CD8 + T cell infiltrates and PD-1 expression correlate with improved ORR; upregulation of immune-related pathways correlate with improved efficacy; Hedgehog pathway expression correlate with resistance. Exploration of this combination in selected sarcomas, and of Hedgehog signaling as a predictive biomarker, warrants further study in larger cohorts.

Conflict of interest statement

S.P.D. reports consulting or advisory roles at EMD Serono, Amgen, Nektar, Immune Design, GlaxoSmithKline, Incyte, Merck, Adaptimmune, and Immunocore. She reports research funding from EMD Serono, Amgen, Merck, Incyte, Nektar, Bristol-Meyers Squibb, and Deciphera, as well as travel expenses from Adaptimmune, EMD Serono, and Nektar. She also reports participation on a Data Safety Monitoring Board or Advisory Board for GlaxoSmithKline, Nektar, Adaptimmune, and Merck. A.P.C. reports research funding from Chordoma Foundation/Cancer Research Institute, Chordoma Foundation, Lilly Pharmaceuticals, Roche Pharmaceuticals/Ignyta, NantPharma, and Bavarian Nordic, as well as consulting fees from Deciphera, Inhbrx, Bayer Pharmaceuticals, and NovellusDX. He has received honoraria from Medscape and Onclive. M.G. reports consulting fees from pharmaceutical companies: Ayala, Bayer, Boehringer Ingelheim, Daiichi, Epizyme, Karyopharm, Springworks, Tracon and TYME; consultation fees from: Flatiron Health, Guidepoint, GLG, Medscape, More Health, Physicians Education Resource and touchIME; royalty fees from UpToDate; patents with MSKCC (GODDESS PRO); uncompensated research with Foundation Medicine, Rain and Athenex. Grants from Food and Drug Administration (R01 FD005105) and the National Cancer Institute, National Institutes of Health (P30CA008748)—core grant (CCSG shared resources and core facility). C.K. reports consulting fees from Exicure and that her spouse is employed at Daiichi Sankyo. S.M. reports research funding from Ascentage Pharmaand Hutchinson MediPharma Limited. K.T. is on the advisory board at GSK. B.N. reports uncompensated provision of services to Delphi Diagnostics, QuadW Foundation, and Rapafusyn Pharmaceuticals. J.E.C. reports research support from Cytek. T.A. reports stock options for BAYRY, CPRX, GSK, TAK, PFE and JNJ. S.P. reports research funding from Blueprint Medicines and Hutchison MediPharma, as well as consultant fees/honoraria from Deciphera, Daichi Sankyo, Dova Pharmaceuticals, and Epizyme. J.A.L. reports research funding from REPARE Therapeutics, Roche/Genentech, Exelixis, Osteosarcoma Institute and CPRIT, as well as consulting fees from ITM and honoraria from Onclive. He is a NCCN Adolescent and Young Adult Oncology Guidelines Committee Member. W.D.T. reports personal fees from Eli Lilly, personal fees from EMD Serono, personal fees from Mundipharma, personal fees from C4 Therapeutics, personal fees from Daiichi Sankyo, personal fees from Blueprint, personal fees from GlaxoSmithKline, personal fees from Agios Pharmaceuticals, personal fees from NanoCarrier, personal fees from Deciphera, personal fees from Adcendo, personal fees from Ayala Pharmaceuticals, personal fees from Kowa, personal fees from Servier, personal fees from AbMaxBio, outside the submitted work; In addition, Dr. Tap has a patent Companion Diagnostic for CDK4 inhibitors - 14/854,329 pending to MSKCC/SKI, and a patent Enigma and CDH18 as companion Diagnostics for CDK4 inhibition – SKI2016-021-03 pending to MSKCC/SKI and Scientific Advisory Board - Certis Oncology Solutions, Stock Ownership. He is Co-Founder at Atropos Therapeutics with Stock Ownership and on the Scientific Advisory Board at Innova Therapeutics. All other authors declare no potential conflicts.

© 2022. The Author(s).

Figures

Fig. 1. Response to the combination of…
Fig. 1. Response to the combination of nivolumab and bempegaldesleukin in patients with metastatic sarcoma.
A CONSORT diagram. B Waterfall plot of overall best response (% change in sum of length measurements) of target lesions/nodes (RECIST v1.1; n = 77 patients evaluable for efficacy). Horizontal line represent cutoff for partial response (PR). Dot indicates achievement of response according to radiographic assessments despite classification as PD in non-target lesions (per RECIST v1.1). Two patients with progressive disease had target lesions that were not evaluable by RECIST. C Progression-free survival (PFS) for each patient (n = 77 patients). PFS is listed for patients without progression longer than 1 year and for patients who did not yet reach progression (indicated by + ). Dashed line indicates 24 weeks. ASPS alveolar soft part sarcoma, UPS undifferentiated pleomorphic sarcoma, MFS myxofibrosarcoma. Source data are provided as a Source Data file.
Fig. 2. Immune cell content in on-treatment…
Fig. 2. Immune cell content in on-treatment samples differentiates response.
AC Percent (A) PD-1-positive cells (Baseline n = 5 patients [PR], 12 [SD], 31 [PD]; On-Treatment n = 3 [PR], 7 [SD], 23 [PD]), (B) CD8-positive T cells (Baseline n = 4 patients [PR], 11 [SD], 32 [PD]; On-Treatment n = 3 [PR], 7 [SD], 22 [PD]), (C) change in PD-1-positive cells (n = 2 patients [PR], 6 [SD], 20 [PD]), as determined by IHC and categorized by best response and sample time point. AC Colors indicate trial cohort. ASPS alveolar soft part sarcoma, UPS undifferentiated pleomorphic sarcoma, MFS myxofibrosarcoma. P values are nominal and derived from linear model of positive cells with ORR including sarcoma subtype as a covariate. Boxplot shows the median with hinges at 25th and 75th percentile with whiskers extending to smallest or largest value, no more than 1.5 times interquartile range from the hinges. All values are shown with points. D, F Immune markers in baseline (n = 41 patients) and on-treatment samples (n = 38 patients), respectively, clustered using Manhattan distance and Ward D clustering on the immune populations from RNA-seq (bottom heatmap), with color coding for best response (top row), quartile of percent positive cells for 5 immune markers as detected using IHC, and immune clusters according to the dendrogram. E, G Kaplan–Meier plot of progression-free survival of 3 and 2 immune clusters defined in D and F, respectively. Logrank p values shown for significant comparisons (Baseline n = 8 patients [A], 6 patients [B], n = 27 [C], On-Treatment n = 9 patients [A], On-Treatment n = 29 [C]). Source data are provided as a Source Data file.
Fig. 3. Differentially expressed pathways in partial…
Fig. 3. Differentially expressed pathways in partial responders.
A Volcano plot of differential expression between partial responders (n = 7 patients) and non-responders (n = 41 patients). Model included trial cohort, patient, purity, and sample time point as covariates. Points represent genes. Genes in Hallmark hedgehog pathway are highlighted in red. B Top 10 upregulated and all 5 downregulated Hallmark pathways between partial responders (n = 7 patients) and non-responders (n = 41 patients). Analysis performed using fgsea in R. All pathways are significantly enriched (BH adjusted p < 0.05). C Heatmap of ssGSEA scores of baseline (n = 41 patients) and on-treatment (n = 38 patients) expression in individual patients for top enriched pathways in (B) using the leading-edge genes from the fgsea analysis. D, E. Kaplan–Meier plot of progression-free survival of patients divided into four groups depending on the amount CD8 + T cells and ssGSEA score of hedgehog pathway enrichment across cohort (high versus low, split by median). Scores derived from expression at baseline in (D) and on-treatment samples in (E). Note, cross-validation analysis does not output p values. Source data are provided as a Source Data file.

References

    1. Anderson WJ, Doyle LA. Updates from the 2020 World Health Organization classification of soft tissue and bone tumours. Histopathology. 2021;78:644–657. doi: 10.1111/his.14265.
    1. Siegel DA, et al. Pediatric cancer mortality and survival in the United States, 2001–2016. Cancer. 2020;126:4379–4389. doi: 10.1002/cncr.33080.
    1. Seddon B, et al. Gemcitabine and docetaxel versus doxorubicin as first-line treatment in previously untreated advanced unresectable or metastatic soft-tissue sarcomas (GeDDiS): a randomised controlled phase 3 trial. Lancet Oncol. 2017;18:1397–1410. doi: 10.1016/S1470-2045(17)30622-8.
    1. Tap WD, et al. Effect of doxorubicin plus olaratumab vs doxorubicin plus placebo on survival in patients with advanced soft tissue sarcomas: the ANNOUNCE Randomized clinical trial. JAMA. 2020;323:1266–1276. doi: 10.1001/jama.2020.1707.
    1. Schoffski P, et al. Eribulin versus dacarbazine in previously treated patients with advanced liposarcoma or leiomyosarcoma: a randomised, open-label, multicentre, phase 3 trial. Lancet. 2016;387:1629–1637. doi: 10.1016/S0140-6736(15)01283-0.
    1. Havel JJ, Chowell D, Chan TA. The evolving landscape of biomarkers for checkpoint inhibitor immunotherapy. Nat. Rev. Cancer. 2019;19:133–150. doi: 10.1038/s41568-019-0116-x.
    1. Wilky BA, et al. Axitinib plus pembrolizumab in patients with advanced sarcomas including alveolar soft-part sarcoma: a single-centre, single-arm, phase 2 trial. Lancet Oncol. 2019;20:837–848. doi: 10.1016/S1470-2045(19)30153-6.
    1. Tawbi HA, et al. Pembrolizumab in advanced soft-tissue sarcoma and bone sarcoma (SARC028): a multicentre, two-cohort, single-arm, open-label, phase 2 trial. Lancet Oncol. 2017;18:1493–1501. doi: 10.1016/S1470-2045(17)30624-1.
    1. D’Angelo SP, et al. Nivolumab with or without ipilimumab treatment for metastatic sarcoma (Alliance A091401): two open-label, non-comparative, randomised, phase 2 trials. Lancet Oncol. 2018;19:416–426. doi: 10.1016/S1470-2045(18)30006-8.
    1. Kelly CM, et al. Objective response rate among patients with locally advanced or metastatic sarcoma treated with talimogene laherparepvec in combination with pembrolizumab: a phase 2 clinical trial. JAMA Oncol. 2020;6:402–408. doi: 10.1001/jamaoncol.2019.6152.
    1. D’Angelo SP, et al. Prevalence of tumor-infiltrating lymphocytes and PD-L1 expression in the soft tissue sarcoma microenvironment. Hum. Pathol. 2015;46:357–365. doi: 10.1016/j.humpath.2014.11.001.
    1. Petitprez F, et al. B cells are associated with survival and immunotherapy response in sarcoma. Nature. 2020;577:556–560. doi: 10.1038/s41586-019-1906-8.
    1. Berghuis D, et al. Pro-inflammatory chemokine-chemokine receptor interactions within the Ewing sarcoma microenvironment determine CD8(+) T-lymphocyte infiltration and affect tumour progression. J. Pathol. 2011;223:347–357. doi: 10.1002/path.2819.
    1. Feng Y, et al. Expression of programmed cell death ligand 1 (PD-L1) and prevalence of tumor-infiltrating lymphocytes (TILs) in chordoma. Oncotarget. 2015;6:11139–11149. doi: 10.18632/oncotarget.3576.
    1. Fujii H, et al. CD8(+) tumor-infiltrating lymphocytes at primary sites as a possible prognostic factor of cutaneous angiosarcoma. Int J. Cancer. 2014;134:2393–2402. doi: 10.1002/ijc.28581.
    1. Rusakiewicz S, et al. Immune infiltrates are prognostic factors in localized gastrointestinal stromal tumors. Cancer Res. 2013;73:3499–3510. doi: 10.1158/0008-5472.CAN-13-0371.
    1. Sorbye SW, et al. Prognostic impact of lymphocytes in soft tissue sarcomas. PLoS One. 2011;6:e14611. doi: 10.1371/journal.pone.0014611.
    1. Diab A, et al. Bempegaldesleukin (NKTR-214) plus nivolumab in patients with advanced solid tumors: phase i dose-escalation study of safety, efficacy, and immune activation (PIVOT-02) Cancer Disco. 2020;10:1158–1173. doi: 10.1158/-19-1510.
    1. Bentebibel SE, et al. A first-in-human study and biomarker analysis of NKTR-214, a novel IL2Rbetagamma-biased cytokine, in patients with advanced or metastatic solid tumors. Cancer Disco. 2019;9:711–721. doi: 10.1158/-18-1495.
    1. Becht E, et al. Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression. Genome Biol. 2016;17:218. doi: 10.1186/s13059-016-1070-5.
    1. Korotkevich, G., Sukhov, V. & Sergushichev, A. Fast gene set enrichment analysis. bioRxiv. (2019). .
    1. Cancer Genome Atlas Research Network. Electronic address, e. d. s. c. & Cancer Genome Atlas Research, N. Comprehensive and integrated genomic characterization of adult soft tissue sarcomas. Cell 171, 950–965 e928 (2017).
    1. Yarchoan M, Hopkins A, Jaffee EM. Tumor mutational burden and response rate to PD-1 inhibition. N. Engl. J. Med. 2017;377:2500–2501. doi: 10.1056/NEJMc1713444.
    1. Rizvi NA, et al. Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science. 2015;348:124–128. doi: 10.1126/science.aaa1348.
    1. Snyder A, Wolchok JD, Chan TA. Genetic basis for clinical response to CTLA-4 blockade. N. Engl. J. Med. 2015;372:783. doi: 10.1056/NEJMc1415938.
    1. Taube JM, et al. Association of PD-1, PD-1 ligands, and other features of the tumor immune microenvironment with response to anti-PD-1 therapy. Clin. Cancer Res. 2014;20:5064–5074. doi: 10.1158/1078-0432.CCR-13-3271.
    1. Turajlic S, et al. Insertion-and-deletion-derived tumour-specific neoantigens and the immunogenic phenotype: a pan-cancer analysis. Lancet Oncol. 2017;18:1009–1021. doi: 10.1016/S1470-2045(17)30516-8.
    1. Chowell D, et al. Patient HLA class I genotype influences cancer response to checkpoint blockade immunotherapy. Science. 2018;359:582–587. doi: 10.1126/science.aao4572.
    1. McGranahan N, et al. Allele-specific HLA loss and immune escape in lung cancer evolution. Cell. 2017;171:1259–1271 e1211. doi: 10.1016/j.cell.2017.10.001.
    1. Zaretsky JM, et al. Mutations associated with acquired resistance to PD-1 blockade in melanoma. N. Engl. J. Med. 2016;375:819–829. doi: 10.1056/NEJMoa1604958.
    1. Painter CA, et al. The Angiosarcoma Project: enabling genomic and clinical discoveries in a rare cancer through patient-partnered research. Nat. Med. 2020;26:181–187. doi: 10.1038/s41591-019-0749-z.
    1. Keung EZ, et al. Correlative analyses of the SARC028 trial reveal an association between sarcoma-associated immune infiltrate and response to pembrolizumab. Clin. Cancer Res. 2020;26:1258–1266. doi: 10.1158/1078-0432.CCR-19-1824.
    1. Tumeh PC, et al. PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature. 2014;515:568–571. doi: 10.1038/nature13954.
    1. Gonzalez H, Hagerling C, Werb Z. Roles of the immune system in cancer: from tumor initiation to metastatic progression. Genes Dev. 2018;32:1267–1284. doi: 10.1101/gad.314617.118.
    1. Faiao-Flores F, et al. Targeting the hedgehog transcription factors GLI1 and GLI2 restores sensitivity to vemurafenib-resistant human melanoma cells. Oncogene. 2017;36:1849–1861. doi: 10.1038/onc.2016.348.
    1. Gan GN, et al. Hedgehog signaling drives radioresistance and stroma-driven tumor repopulation in head and neck squamous cancers. Cancer Res. 2014;74:7024–7036. doi: 10.1158/0008-5472.CAN-14-1346.
    1. Sims-Mourtada J, et al. Hedgehog: an attribute to tumor regrowth after chemoradiotherapy and a target to improve radiation response. Clin. Cancer Res. 2006;12:6565–6572. doi: 10.1158/1078-0432.CCR-06-0176.
    1. Chakrabarti J, et al. Hedgehog signaling induces PD-L1 expression and tumor cell proliferation in gastric cancer. Oncotarget. 2018;9:37439–37457. doi: 10.18632/oncotarget.26473.
    1. Grund-Groschke S, et al. Epidermal activation of Hedgehog signaling establishes an immunosuppressive microenvironment in basal cell carcinoma by modulating skin immunity. Mol. Oncol. 2020;14:1930–1946. doi: 10.1002/1878-0261.12758.
    1. Hanna A, et al. Inhibition of Hedgehog signaling reprograms the dysfunctional immune microenvironment in breast cancer. Oncoimmunology. 2019;8:1548241. doi: 10.1080/2162402X.2018.1548241.
    1. Sekulic A, et al. Efficacy and safety of vismodegib in advanced basal-cell carcinoma. N. Engl. J. Med. 2012;366:2171–2179. doi: 10.1056/NEJMoa1113713.
    1. Von Hoff DD, et al. Inhibition of the hedgehog pathway in advanced basal-cell carcinoma. N. Engl. J. Med. 2009;361:1164–1172. doi: 10.1056/NEJMoa0905360.
    1. Otsuka A, et al. Hedgehog pathway inhibitors promote adaptive immune responses in basal cell carcinoma. Clin. Cancer Res. 2015;21:1289–1297. doi: 10.1158/1078-0432.CCR-14-2110.
    1. MIT. TEMPO: CCS Research Pipeline for Whole-Genome and Whole-Exome Sequencing. (2019).
    1. Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv: Genomics (2013).
    1. McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res.20, 1297–1303. 10.1101/gr.107524.110 (2010).
    1. Benjamin, D. et al. Calling somatic SNVs and indels with Mutect2. bioRxiv. 10.1101/861054 (2019).
    1. Kim, S. et al. Strelka2: fast and accurate calling of germline and somatic variants. Nat. Methods15, 591–594 10.1038/s41592-018-0051-x (2018).
    1. Smit, A. F. A., Hubley, R. & Green, P. RepeatMasker Open-4.0. (2013–2015).
    1. Karczewski KJ, et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature. 2020;581:434–443. doi: 10.1038/s41586-020-2308-7.
    1. Chakravarty, D. et al. OncoKB: A precision oncology knowledge base. JCO Precis. Oncol.2017. 10.1200/PO.17.00011 (2017).
    1. Shen R, Seshan VE. FACETS: allele-specific copy number and clonal heterogeneity analysis tool for high-throughput DNA sequencing. Nucleic Acids Res. 2016;44:e131. doi: 10.1093/nar/gkw520.
    1. Jonsson, P. MSKCC / facets-suite. (2019).
    1. Shukla SA, et al. Comprehensive analysis of cancer-associated somatic mutations in class I HLA genes. Nat. Biotechnol. 2015;33:1152–1158. doi: 10.1038/nbt.3344.
    1. Jurtz V, et al. NetMHCpan-4.0: Improved Peptide-MHC Class I Interaction predictions integrating eluted ligand and peptide binding affinity data. J. Immunol. (Baltim., Md.: 1950) 2017;199:3360–3368. doi: 10.4049/jimmunol.1700893.
    1. Dobin A, et al. STAR: ultrafast universal RNA-seq aligner. Bioinforma. (Oxf., Engl.) 2013;29:15–21. doi: 10.1093/bioinformatics/bts635.
    1. Flicek, P. et al. Ensembl 2014. Nucleic Acids Res.42, D749–D755 (2014).
    1. Yates AD, et al. Ensembl 2020. Nucleic Acids Res. 2020;48:D682–D688. doi: 10.1093/nar/gkz1138.
    1. Broad Institute. Picard Toolkit. (2019).
    1. D. Nicorici, et al. FusionCatcher – a tool for finding somatic fusion genes in paired-end RNA-sequencing data. bioRxiv. (2014). .
    1. Uhrig S, et al. Accurate and efficient detection of gene fusions from RNA sequencing data. Genome Res. 2021;31:448–460. doi: 10.1101/gr.257246.119.
    1. Bray NL, Pimentel H, Melsted P, Pachter L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 2016;34:525–527. doi: 10.1038/nbt.3519.
    1. Sturm G, et al. Comprehensive evaluation of transcriptome-based cell-type quantification methods for immuno-oncology. Bioinforma. (Oxf., Engl.) 2019;35:i436–i445. doi: 10.1093/bioinformatics/btz363.
    1. Pimentel H, Bray NL, Puente S, Melsted P, Pachter L. Differential analysis of RNA-seq incorporating quantification uncertainty. Nat. Methods. 2017;14:687–690. doi: 10.1038/nmeth.4324.
    1. Dolgalev, I. msigdbr: MSigDB Gene Sets for Multiple Organisms in a Tidy Data Format. R package version 7.2.1. (2020).
    1. Liberzon A, et al. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst. 2015;1:417–425. doi: 10.1016/j.cels.2015.12.004.
    1. Hanzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinforma. 2013;14:7. doi: 10.1186/1471-2105-14-7.
    1. Friedman J, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 2010;33:1–22. doi: 10.18637/jss.v033.i01.
    1. MiLaboratory LLC. MiXCR: a universal tool for fast and accurate analysis of T- and B- cell receptor repertoire sequencing data. (2018).
    1. Shugay, M. VDJtools: a framework for post-analysis of repertoire sequencing data. (2015).

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