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Duvelisib plus romidepsin in relapsed/refractory T cell lymphomas: a phase 1b/2a trial

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Abstract

PI3K-δ inhibitors have shown impressive activity in lymphoid malignancies but have been hampered by autoimmune and infectious toxicities, leading to market withdrawals. We previously demonstrated activity of the PI3K-δγ inhibitor duvelisib in T cell lymphomas (TCLs) that was associated with inflammatory adverse events. As reported here, we conducted a phase 1b/2a study of duvelisib in combination with either romidepsin (n = 66) or bortezomib (n = 32) in patients with relapsed/refractory TCL and found that the addition of romidepsin, but not bortezomib, appeared to increase efficacy while attenuating PI3K inhibitor-driven toxicity. The primary endpoint of the study was to determine the safety and maximum tolerated dose of duvelisib, which was 75 mg twice daily when combined with romidepsin versus 25 mg twice daily when combined with bortezomib. The most common adverse events were neutropenia (42%, 25/59) and fatigue (37%, 22/59) in patients treated with duvelisib and romidepsin and diarrhea (48%, 11/23) and neutropenia (30%, 7/23) in patients treated with duvelisib and bortezomib. Duvelisib and romidepsin resulted in less grade 3/4 hepatotoxicity (14%, 8/59) compared to 40% (14/35) in our previous study with duvelisib monotherapy. This was associated with reductions in circulating inflammatory mediators and myeloid cell inflammatory gene expression. Secondary endpoints of overall and complete response rates were 55% (35/64) and 34% (22/64) for patients treated with duvelisib and romidepsin and 34% (11/32) and 13% (4/32) for patients treated with duvelisib and bortezomib. Among patients with peripheral T cell lymphomas (PTCLs), overall and complete response rates of duvelisib and romidepsin were 56% (27/48) and 44% (21/48), respectively, with exploratory analyses showing increased response rates in patients with a follicular helper T cell subtype. These findings support further development of combined PI3K and histone deacetylase (HDAC) inhibition in TCLs and suggest a unique strategy to enable PI3K inhibitor-based combinations for additional patient populations. ClinicalTrials.gov identifier: NCT02783625.

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Fig. 1: CONSORT diagram.
Fig. 2: Romidepsin + duvelisib is a highly active combination treatment strategy in patients with PTCL.
Fig. 3: AEs in patients treated with romidepsin + duvelisib are associated with myeloid inflammation.

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Data availability

For original clinical data, contact horwitzs@mskcc.org. De-identified participant data can be shared. External data requests will be answered within 1 month. The study protocols are included as a data supplement available with the online version of this article. Bulk RNA sequencing data are available in the Sequence Read Archive (PRJNA1098020). Single-cell transcriptomic data are available in the Gene Expression Omnibus (GSE266760). Source data are provided with this paper.

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Acknowledgements

This was an investigator-initiated study funded initially by Infinity Pharmaceuticals and subsequently by Verastem and Secura Bio as well as by Leukemia & Lymphoma Society (LLS) Specialized Center of Research (SCOR) 7011-16, LLS SCOR 7026-21 and National Cancer Institute (NCI) R35 CA231958. Medical writing support was provided by S. Lalchandani. S.M.H. was supported by Nonna’s Garden Foundation. A.J.N. was supported by NCI R00 CA256497. S.A.V. was supported by a Steven A. Greenberg Lymphoma Research Grant. We would like to thank our patients and their families as well as the faculty and research staff of our various institutions.

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Author notes

  1. These authors contributed equally: Jahan Rahman, Ran Xu.

Authors and Affiliations

  1. Lymphoma Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Steven M. Horwitz, Natasha Galasso, Nivetha Ganesan, Theresa Davey, Helen Hancock, Leslie Perez, Catherine Maccaro, Alexandra Bahgat, Evan Marzouk, Elizabeth Cathcart, Alison Moskowitz, Ariela Noy, Anita Kumar & Santosha A. Vardhana

  2. New York Presbyterian Hospital-Weill Cornell Medical College, New York, NY, USA

    Steven M. Horwitz & Santosha A. Vardhana

  3. Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA

    Ajit J. Nirmal, Ran Xu, Eric Jacobsen, David C. Fisher, Zoe Ciantra, Jackson W. Appelt, Christopher Trevisani, Sam Amaka & David M. Weinstock

  4. Department of Biostatistics and Epidemiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Jahan Rahman & Esther Drill

  5. Department of Medicine, Division of Oncology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA

    Neha Mehta-Shah

  6. Division of Oncology, Stanford University, Stanford, CA, USA

    Youn H. Kim & Michael Khodadoust

  7. Department of Dermatology, Stanford University, Stanford, CA, USA

    Youn H. Kim & Michael Khodadoust

  8. BostonGene Corporation, Boston, MA, USA

    Nikita Kotlov, Anastasia Nikitina, Olga Kudryashova, Valeria Zubareva, Ksenia Zornikova, Nara Shin, Maria Sorokina, Sandrine Degryse, Ekaterina Postovalova & Aleksander Bagaev

  9. Department of Pediatrics and Immune Discovery & Modeling Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Kinga Hosszu, Devin McAvoy & Jaap J. Boelens

  10. Stem Cell Transplantation and Cellular Therapies, MSK Kids, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Jaap J. Boelens

  11. State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center Collaborative Innovation Center for Cancer Medicine, Guangzhou, China

    Wenchao Wu

  12. Merck and Co., Rahway, NJ, USA

    David M. Weinstock

  13. Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Santosha A. Vardhana

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  1. Steven M. Horwitz
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  2. Ajit J. Nirmal
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  3. Jahan Rahman
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  4. Ran Xu
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  5. Esther Drill
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  36. Wenchao Wu
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  42. Santosha A. Vardhana
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S.M.H. designed the study, with input from Infinity Pharmaceuticals and Verastem. S.M.H., E.D., N. Ganesan, N. Galasso, T.D., H.H., L.P., C.M., A. Bahgat, E.M., E.C., Z.C., J.W.A., C.T. and S.A. contributed to clinical trial execution and monitoring. S.M.H., A.M., A. Noy, A.K., E. J., D.C.F., N.M.-S., Y.K., W.W. and M.K. enrolled patients on the study. J.R., R.X., N.K., A. Nirmal, O.K., V.Z., K.Z., N.S., M.S., S.D., E.P. and A. Bagaev analyzed genomic and transcriptomic data. A.J.N., R.X. and D.M.W. performed in vitro assays and in vivo xenograft studies. K.H., D.M. and J.J.B. performed Olink proteomic analysis. S.M.H., A.J.N., J.R., R.X., D.M.W. and S.A.V. wrote the manuscript, with writing support from Secura Bio. All authors edited the manuscript and approved of all edits before submission.

Corresponding authors

Correspondence to Steven M. Horwitz or Santosha A. Vardhana.

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Competing interests

D.M.W. is an employee of Merck and Co.; owns equity in Merck and Co., Bantam, Ajax and Travera; received consulting fees from AstraZeneca, Secura Bio, Novartis and Roche/Genentech; and received research support from Daiichi Sankyo, AstraZeneca, Verastem, AbbVie, Novartis, Abcura and Surface Oncology. S.A.V. has served as an advisor for Immunai and has received consulting fees from ADC Therapeutics and Koch Disruptive Technologies. J.J.B. received consulting fees from Sobi, Omeros, Bluebird Bio, Sanofi, Immusoft, SmartImmune, Bluerock and Advanced Clinical. N.M.-S. has received institutional clinical trial funding from AstraZeneca, Bristol Myers Squibb, Celgene, C4 Therapeutics, Corvus Pharmaceuticals, Daiichi Sankyo, Genentech/Roche, Innate Pharmaceuticals, Secura Bio/Verastem, Yingli Pharmaceuticals and Dizal Pharmaceuticals. N.M.-S. has also received compensation for service as a consultant for AstraZeneca, Secura Bio/Verastem, Daiichi Sankyo, C4 Therapeutics, Genentech, Janssen, Karyopharm Therapeutics and Kyowa Hakko Kirin. N.M.-S. is funded as a Scholar in Clinical Research through the Leukemia & Lymphoma Society, and this study was reviewed as part of the ASCO/AACR Workshop. S.M.H. has received research support from ADC Therapeutics, Affimed, C4, Celgene, CRISPR Therapeutics, Daiichi Sankyo, Dren, Kyowa Hakko Kirin, Millennium/Takeda, Seattle Genetics and Secura Bio. S.M.H. has also received consulting fees from Affimed, Abcuro, Corvus, Daiichi Sankyo, Kyowa Hakko Kirin, ONO Pharmaceuticals, SeaGen, Secura Bio, Takeda and Yingli Pharmaceuticals. A.J.M. has received research support from BeiGene, Seattle Genetics, Merck, Bristol Myers Squibb, Incyte, Affimed and AstraZeneca. A.J.M. has also received consulting fees from Affimed, Merck, Seattle Genetics and Takeda. The remaining authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Expanded enrollment diagram for NCT#02783625.

Expanded diagram showing enrollment on NCT#02783625.

Extended Data Fig. 2 Study design and outcomes of NCT #02783625 in patients with PTCL and CTCL.

(a) Swimmer’s plot depicting responses of patients with PTCL to bortezomib + duvelisib (Arm B). (b) Kaplan-Meier curve showing event-free survival of all PTCL patients stratified by treatment arm. (c) Swimmer’s plot depicting responses of patients with CTCL to romidepsin + duvelisib (Arm a) or bortezomib + duvelisib (Arm B) as indicated. (d) Kaplan-Meier curve showing event-free survival of patients with CTCL, stratified by treatment arm.

Extended Data Fig. 3 A tumor-specific T follicular-helper phenotype is associated with response to romidepsin and duvelisib.

(a) Best response of patients with PTCL receiving romidepsin + duvelisib in Arm A compared with NCT00426764, stratified by whether PTCL were of follicular helper subtype. (b) Oncoprint of somatic mutations observed in pre-treatment biopsy samples from patients with PTCL, organized by treatment response and frequency. Stars indicate genes in which mutations are enriched in either responders (R) or non-responders (NR). (c) Lollipop plot of individual mutations in genes commonly mutated in PTCL with Tfh features. (d) Genes enriched in responders versus non-responders in pre-treatment samples obtained from patients with PTCL. (e) B-cell proportions estimated by CIBERSORT deconvolution in pre-treatment samples from responders (N=13) versus non-responders (N=10). Data are presented as box plots with center depicting median, bounds of box depicting interquartile range, and whiskers depicting minima and maxima. (f) Enrichment of IL21 in pre-treatment bulk RNA-seq samples from PTCL responders (N=5) versus non-responders (N=7) to romidepsin and duvelisib. Data are presented as box plots with center depicting median, bounds of box depicting interquartile range, and whiskers depicting minima and maxima. P values calculated by Chi squared test (a), Wald Test corrected for False Discovery Rate using the Benjamini-Hochberg procedure (d), student’s t-test (e), and two-sided Wilcoxon Rank-Sum Test (f).

Extended Data Fig. 4 Pre-treatment predictors of response to romidepsin and duvelisib in patients with PTCL and CTCL.

(a) Dot plot depicting enrichment of MSigDB genesets in pre-treatment bulk RNA-seq from responding vs. non-responding patients with PTCL. (b) Oncoprint of somatic mutations observed in pre-treatment biopsy samples from patients with CTCL, organized by treatment response and frequency. (c) Lollipop plot of individual mutations in NFKB2 in patients with CTCL. (d) Copy number plot depicting alterations by chromosome in patients with CTCL. RET and NFKB2 genes loci are indicated. (e) Genes enriched in responders versus non-responders in pre-treatment samples obtained from patients with CTCL. P values determined by Wald Test corrected for False Discovery Rate using the Benjamini-Hochberg procedure (e).

Extended Data Fig. 5 Mechanisms of response to romidepsin and duvelisib in PTCL xenografts and patient samples.

(a) Experimental design and survival of mice xenografted with an AITL xenograft and treated with either vehicle, romidepsin, duvelisib, or the combination. (b) Decrease in mitosis-associated MSigDB genesets in on-treatment versus pre-treatment samples from PTCL patients who responded to therapy (left) and AITL xenografts (right). Normalized enrichment score (NES), false discovery rate (FDR) and proportion of genes expressed in each gene set are as described. P values calculated using log-rank test (a) and estimated as the number of random gene set enrichment scores with the same or more extreme values divided by the total number of randomly generated gene sets, corrected for False Discovery Rate using the Benjamini-Hochberg procedure (b).

Extended Data Fig. 6 JAK/STAT-driven resistance to romidepsin and duvelisib.

(a) Proportion of patients with activation mutations or amplifications of JAK/STAT genes, stratified by response. (b) Copy number plot depicting alterations by chromosome in patients with PTCL. STAT1 locus on chromosome 2 is highlighted. (c) Lollipop plot of pre-treatment mutations in JAK1 and JAK3 in non-responders to romidepsin + duvelisib. (d) UMAP of CD45+ cells isolated from pre- and on-treatment peripheral blood samples obtained from patients with high-burden circulating disease receiving romidepsin + duvelisib. (e) Expression of T-cell markers on malignant cells from Patients Y and Z visualized in 2-dimensional UMAP space. (f) Differently expressed genes in malignant cells isolated from the peripheral blood of a patient who did not respond to romidepsin + duvelisib. (g) Expression of IL6_JAK_STAT3 genes in pre-treatment and on-treatment circulating malignant T-cells from patients with (Z) or without (Y) response to romidepsin + duvelisib. (h) Gene set enrichment analysis of HALLMARK_IL6_JAK_STAT3_SIGNALING in PDX tumors refractory to duvelisib monotherapy. P values determined by Fisher’s exact test (a), two-sided Wilcoxon Rank-Sum Test with Bonferroni correction for multiple comparisons (f), student’s t-test (g), and estimated as the number of random gene set enrichment scores with the same or more extreme values divided by the total number of randomly generated gene sets, corrected for False Discovery Rate using the Benjamini-Hochberg procedure (h).

Extended Data Fig. 7 PI3K/mTORC1 reactivation in tumors resistant to romidepsin and duvelisib.

(a) Emergence of clones with indicated mutations in patients treated with romidepsin + duvelisib who ultimately experienced disease progression. (b) Expression of PI3K_AKT_MTOR genes in pre-treatment and on-treatment circulating malignant T-cells from patients with (Z) or without (Y) response to romidepsin + duvelisib. (c) Efficient CRISPR editing of TSC2 and PTEN expression by 2 independent guide RNAs as shown by western blot. (d) Sensitivity of OCI-ly13.2 cells expressing Cas9 and doxycycline-inducible sgRNA targeting TSC2 (above) or PTEN (below) to duvelisib in vitro, with (N=3) or without (N=3) the addition of doxycycline to induce gene editing. Data presented as mean values +/- SD and are representative of two independent experiments. (e) Decrease in phagocytosis-associated MSigDB genesets in on-treatment versus pre-treatment samples from PTCL patients who did not respond to therapy. (f) Estimated intratumoral macrophage abundance, based on CIBERSORT deconvolution, in pre-treatment and on-treatment biopsies of patients undergoing treatment with romidepsin + duvelisib, stratified by response (non-responder pre-treatment N=11, non-responder on-treatment N=10, responder pre-treatment N=12, responder on-treatment N=10). Data are presented as box plots with center depicting median, bounds of box depicting interquartile range, and whiskers depicting minima and maxima. P values determined using student’s t-test (b) and estimated as the number of random gene set enrichment scores with the same or more extreme values divided by the total number of randomly generated gene sets, corrected for False Discovery Rate using the Benjamini-Hochberg procedure (e).

Source data

Extended Data Fig. 8 Persistent NF-kB signaling and proliferation in patients with clinically significant adverse events following romidepsin and duvelisib.

(a) Changes in plasma analytes between C1D8 and C1D1 samples in patients treated with duvelisib or duvelisib + romidepsin as indicated. (b) UMAP of CD45+ cells isolated from pre- and on-treatment peripheral blood samples obtained from patients without circulating disease receiving romidepsin + duvelisib. (c) Dot plot depicting average expression and frequency of expression of genes defining CD4+T, CD8+T, and myeloid cell subsets as indicated. (d) Differentially expressed genes in pre-treatment peripheral blood CD14+ monocytes from patient experiencing clinically significant (Grade 2-3) versus non-significant (Grade 0-1) hepatotoxicity. P values calculated using two-sided Wilcoxon Rank-Sum Test with Bonferroni correction for multiple comparisons (d).

Supplementary information

Supplementary information

Supplementary Fig. 1, Supplementary Tables 1–5 and Protocol.

Reporting Summary

Source data

Source Data Extended Data Fig./Table 7

Unprocessed western blots for Extended Data Fig. 7.

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Horwitz, S.M., Nirmal, A.J., Rahman, J. et al. Duvelisib plus romidepsin in relapsed/refractory T cell lymphomas: a phase 1b/2a trial. Nat Med 30, 2517–2527 (2024). https://doi.org/10.1038/s41591-024-03076-6

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