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Targeting a lineage-specific PI3Kɣ–Akt signaling module in acute myeloid leukemia using a heterobifunctional degrader molecule

Nature Cancer volume 5, pages 1082–1101 (2024)Cite this article

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Abstract

Dose-limiting toxicity poses a major limitation to the clinical utility of targeted cancer therapies, often arising from target engagement in nonmalignant tissues. This obstacle can be minimized by targeting cancer dependencies driven by proteins with tissue-restricted and/or tumor-restricted expression. In line with another recent report, we show here that, in acute myeloid leukemia (AML), suppression of the myeloid-restricted PIK3CG/p110γ–PIK3R5/p101 axis inhibits protein kinase B/Akt signaling and compromises AML cell fitness. Furthermore, silencing the genes encoding PIK3CG/p110γ or PIK3R5/p101 sensitizes AML cells to established AML therapies. Importantly, we find that existing small-molecule inhibitors against PIK3CG are insufficient to achieve a sustained long-term antileukemic effect. To address this concern, we developed a proteolysis-targeting chimera (PROTAC) heterobifunctional molecule that specifically degrades PIK3CG and potently suppresses AML progression alone and in combination with venetoclax in human AML cell lines, primary samples from patients with AML and syngeneic mouse models.

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Fig. 1: AML cells exhibit a selective dependency on PIK3CG and its regulatory subunit PIK3R5.
Fig. 2: PIK3CG or PIK3R5 suppression potentiates the antileukemia effect of venetolax.
Fig. 3: PIK3CG and PIK3R5 selectively control Akt signaling in AML.
Fig. 4: PROTAC-based PIK3CG degradation is a promising strategy to durably suppress Akt signaling in AML.
Fig. 5: Degradation of PIK3CG demonstrated superior cytotoxic performance relative to existing small-molecule inhibitors of PIK3CG.
Fig. 6: PIK3CG degradation potentiates the effect of venetoclax in multiple AML models.

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Data availability

Gene expression data for normal and malignant tissues were obtained from the GTEx gene expression dataset and accessed through the Gepia portal. Gene dependency data were obtained from the DepMap dependency dataset. Data analyses were performed using R or GraphPad Prism 8. Genetic and clinical features were explored using star counts and clinical annotation from the Beat AML 1.0 cohort. The Beat AML 1.0 cohort was used also to assess the gene expression of patients with AML with available transcriptomic profiling. Raw counts from the Beat AML 1.0 cohort were available from the NIH GDC portal and genetic and clinical annotations were available from the supplementary information provided in the original article reporting this cohort26. TCGA data were available from the NIH GDC portal. The RNA-seq-based profiling of the AML cell lines treated with ARM165 is available from GSE260759. Queried gene sets were from the MSigDB Hallmark and C2 geneset libraries. The H3K SRA database was accessible under accession number SRP103200. All other data supporting the findings of this study are available from the corresponding author on reasonable request. No custom code was generated in the course of this study. Source data are provided with this paper.

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Acknowledgements

We thank the members of the A. Puissant, K. C. Wood and A. R. Martin laboratories for their scientific input. We are indebted to V. Montcuquet, N. Setterblad, C. Doliger and C. Maillard from the Saint-Louis Research Institute Core Facility and to CNRS, University Montpellier and the SynBio3 platform supported by IBiSa and the Chimie Balard Cirimat Carnot Institute. We are grateful to L. H. Castilla for providing us with the Cbfb–MYH11 knock-in mouse model and to M. Wymann for providing us with the PIK3CG-directed antibody and for fruitful suggestions on the project. This work was supported by the ERC Starting and Consolidator programs (AltChem 758848 and DynAML 101088563 to A.P.), the Laurette Fugain association (to A.P.), Amgen Innovations (to A.P.), Fondation ARC (to C.Lo.), ATIP-Avenir 2022 Ligue Nationale contre le Cancer (to L. Be.) and the INCA PLBIO program (PLBIO20-246 to A.P. and C.Lo.; PLBIO20-074 to C.Lo.). This work was supported by NIH R01CA266389 (to K.C.W. and A.P.) and NIH U54CA274502 (to A.F., D.L.S. and N.J.K.). M.D. is supported by the Bettencourt Schueller Foundation (CCA-INSERM-Bettencourt). A.P. is an FSER laureate and a recipient of the Brigitte Mérand, Jean Valade and Tourre awards. A.P., R.I., L.Be. and C.L. are supported by the SIRIC InsiTu program (INCa-DGOS-INSERM-ITMO Cancer_18008).

Author information

Author notes

  1. These authors contributed equally: Lois M. Kelly, Justine C. Rutter, Kevin H. Lin.

  2. These authors jointly supervised this work: Anthony R. Martin, Kris C. Wood, Alexandre Puissant.

Authors and Affiliations

  1. INSERM UMR 944, IRSL, Saint-Louis Hospital, Paris Cité University, Paris, France

    Lois M. Kelly, Frank Ling, Matthieu Duchmann, Emmanuelle Latour, Hélène Pasquer, Duong Ho Nhat, Juliette Charles, Federica Namor, Cécile Culeux, Marie Sebert, Lionel Adès, Emmanuel Raffoux, Nina Fenouille, Raphaël Itzykson, Camille Lobry, Lina Benajiba & Alexandre Puissant

  2. Department of Pharmacology and Cancer Biology, Duke University, Durham, NC, USA

    Justine C. Rutter, Kevin H. Lin, Shane T. Killarney, Hazel X. Ang & Kris C. Wood

  3. Quantitative Biosciences Institute (QBI), University of California, San Francisco, San Francisco, CA, USA

    Nadia Arang, Danielle L. Swaney, Nevan J. Krogan & Antoine Forget

  4. Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA, USA

    Nadia Arang, Danielle L. Swaney, Nevan J. Krogan & Antoine Forget

  5. Curie Institute, Mass Spectrometry and Proteomics Facility, PSL Research University, Paris, France

    Bérangère Lombard & Damarys Loew

  6. Gladstone Institutes, San Francisco, California, USA

    Danielle L. Swaney & Nevan J. Krogan

  7. IBMM, University of Montpellier, CNRS, ENSCM, Montpellier, France

    Luc Brunel, Élodie Carretero, Pascal Verdié, Muriel Amblard & Anthony R. Martin

  8. Department of Hematology and Immunology, Saint-Louis Hospital, AP-HP, Paris Cité University, Paris, France

    Sofiane Fodil, Tony Huynh, Marie Sebert, Lionel Adès, Emmanuel Raffoux & Raphaël Itzykson

  9. Clinical Investigation Center, Saint-Louis Hospital, AP-HP, Paris Cité University, Paris, France

    Lina Benajiba

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Contributions

L.M.K., conceptualization, formal analysis, validation, investigation, visualization, methodology and writing—original draft. J.C.R., conceptualization, formal analysis, validation, investigation, visualization and methodology. K.H.L., conceptualization, formal analysis, validation, investigation, visualization and methodology. F.L., investigation and methodology. M.D., resources and investigation. E.L., investigation and methodology. N.A., investigation and methodology. H.P., resources and methodology. D.H.N., investigation and methodology. J.C., investigation. S.T.K., investigation. H.X.A., investigation. F.N., investigation. C.C., resources. B.L., methodology. D.L., methodology. D.L.S., methodology and resources. N.J.K., resources. L. Brunel, methodology and resources. E.C., methodology and resources. P.V., methodology and resources. M.A., methodology and resources. S.F., resources. T.H., resources. M.S., resources. L.A., resources. E.R., resources. N.F., methodology, resources and support. R.I., conceptualization, methodology and investigation. C.L., conceptualization, formal analysis, visualization and methodology. L. Benajiba, conceptualization, investigation, methodology and resources. A.F., conceptualization, investigation, methodology and visualization. A.R.M., conceptualization, formal analysis, supervision, investigation, visualization, methodology and writing—original draft. K.C.W., conceptualization, supervision, funding acquisition, investigation, visualization, methodology, writing—original draft and project administration. A.P., conceptualization, formal analysis, supervision, funding acquisition, investigation, methodology, writing—original draft and project administration.

Corresponding authors

Correspondence to Anthony R. Martin, Kris C. Wood or Alexandre Puissant.

Ethics declarations

Competing interests

The Krogan Laboratory has received research support from Vir Biotechnology, F. Hoffmann-La Roche and Rezo Therapeutics. N.J.K. has financially compensated consulting agreements with Maze Therapeutics. N.J.K. is the president and on the board of directors of Rezo Therapeutics and a shareholder in Tenaya Therapeutics, Maze Therapeutics, Rezo Therapeutics and Interline Therapeutics. K.C.W. is a cofounder, consultant and equity holder at Tavros Therapeutics and Celldom, is a consultant and equity holder at Simple Therapeutics and Decrypt Biomedicine and has performed consulting work for Guidepoint Global, Bantam Pharmaceuticals and Apple Tree Partners. The other authors declare no competing interests.

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Nature Cancer thanks Oliver Hantschel, Ross Levine and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1

a. Comparison of the expression levels of PIK3CG and PIK3R5 across AML and healthy tissues. P-values calculated using one-way ANOVA. Error bars represent mean ± SD. b-d. Expression levels of PIK3CG and PIK3R5 in AML patients from various FAB (b) and genetic (c) subcategories, or at diagnosis versus relapse (d). Median with lower and upper hinges corresponding to the first and third quartiles. The lower and upper whiskers represent the lowest and largest value within 1.5 times the lower and upper interquartile, respectively. The points represent individual values. P-values calculated using two-sided Wilcoxon test and reported on the figure panel. e and f. Spearman correlation between AML cell differentiation state and the expression of PIK3CG and PIK3R5. Correlation between PIK3CG or PIK3R5 expression and ssGSVA score were tested using Spearman’s rank correlation. Two-sided p-values were corrected for multi-testing using the Benjamini & Hochberg method. f. The smooth area corresponds to the 95% confidence interval of the linear regression model. b-f. Data generated using the Beat AML cohort. a-d: n values displayed on the graph.

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Extended Data Fig. 2

a. DNA sanger sequencing of the PIK3CG and PIK3R5 genomic regions targeted by the CRISPR-Cas9 PIK3CG- and PIK3R5-directed guides in OCI-AML2 cells. Sequences were aligned using TIDE online tool to determine the relative efficiency of each sgRNA. Repeated twice with similar results. b. Bioluminescence pictures of three representative mice from Fig. 1j injected with OCI-AML2 infected with either a non-targeting control or a PIK3CG-directed sgRNA. Median bioluminescence is depicted in radiance on days 7, 13, 18 and 22.

Extended Data Fig. 3

a and b. Representative growth inhibition curves, and corresponding IC50 and AUC values (n = 4, A, n = 7, B, mean ± SD after three days of seeding) from Fig. 2c of OCI-AML2 cells transduced with either a non-targeting control, two PIK3CG-directed, or two PIK3R5-directed sgRNAs and treated with increasing concentrations of the indicated targeted therapies or chemotherapy drugs (a), FLT3 inhibitors, gilteritinib and sorafenib, or KIT inhibitors, amuvanib and telatinib (b). Av. = Averaged. c. Bioluminescence pictures of three representative mice from Fig. 2i injected with OCI-AML2 cells harboring a non-targeting control or PIK3CG-directed sgRNA. Median bioluminescence is depicted in radiance on days 17, 24, 27 and 31.

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Extended Data Fig. 4

a. Volcano plots of the PIK3R5-interacting protein pulled down in OCI-AML2, MV4-11, and NOMO-1 cells compared to control. DIA MS data were processed using Spectronaut with default factory setting providing an unpaired t-test p-value for quantified proteins. b. Ranking of proteins with positive infinite ratios (only identified in PIK3R5 pull down) according to the number of peptides identified. Only proteins with a z-test score > 0.95 were included for further analysis. c. Network corresponding to Fig. 3a depicting, in two colors, the PIK3R5-interacting proteins identified in two cell lines (in yellow) or all three cell lines (in orange).

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Extended Data Fig. 5

a. Correlation between gene dependency scores of AKT1 and AKT2 versus PIK3CG in a panel of non-AML cell lines. Data from DepMap CRISPR/Cas9 dependency profiling. Pearson correlation coefficient (ρ) provided to demonstrate no correlation. b. MK-2206 sensitizer and resistor topoisomerase-encoding genes, polymerase-encoding genes, and anti-apoptotic protein-encoding genes identified from a pooled drug-modifier screen conducted in OCI-AML2 cells. Gene-level scores were obtained by averaging sgRNA-level comparisons. Red dot denotes BCL2 identified as a sensitizer gene. All other genes included in the analysis are depicted as blue dots. c. Chemokine/receptor expression heatmap indicating upregulation of both CXCL12/CXCR4 in AML relative to normal tissue. Tumor expression data from TCGA database; normal expression data from GeTex expression database. Data accessed through Gepia gene expression portal.

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Extended Data Fig. 6

Synthetic scheme for the synthesis of the PIK3CG degrader, ARM165. Reagents and conditions: i) LiOH monohydrate, MeOH, H2O, 60 °C, 40 h (94%); ii) Boc-AOc-OH, T3P 50% in ethyl acetate, pyridine, N,N-dimethylformamide, 80 °C, 16 h; iii) TFA, CH2Cl2, r.t. 2 h; iv) Glutaric anhydride, N,N-diisopropylethylamine, toluene, N,N-dimethylformamide, 110 °C, 2 h (62% over 3 steps, ii-iv); v) Compound I, HATU, N,N-diisopropylethylamine, N,N-dimethylformamide, r.t. 16 h (55%).

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Extended Data Fig. 7

a. Synthetic scheme for the synthesis of non-PIK3CG-targeting control compound for ARM165, ARM204. Reagents and conditions are provided below the synthesis scheme. b. Representative growth inhibition curves with IC50s and AUCs (n = 7, mean ± SD), corresponding to the Fig. 5b for AML and non-AML cells treated with increasing doses of ARM204, AZ2, and ARM165.

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Extended Data Fig. 8

a. Representative growth inhibition curves, IC50s, and AUC values n = 7, mean ± SD) corresponding to Fig. 6a of indicated AML cells treated with increasing doses of venetoclax in presence of AZ2 or ARM165 after three days of seeding. b-c. Growth inhibition curves, IC50s, and AUC values (n = 3 biological replicates composed of seven technical replicates, mean ± SD), of OCI-AML2 cells treated with increasing doses of cytarabine, daunorubicin, or venetoclax in combination with 500 nM ARM165. P-values calculated using one-way ANOVA and reported on the figure panel.

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Extended Data Fig. 9

a-b. Toxicity profile of ARM165 treatment in naive mice. Mice were treated with IV injection of 0.051 mg/kg ARM165 for seven consecutive days. Individual mouse weight was measured daily (n = 5 mice per group) (a) and the proportion of each indicated hematopoietic cell fraction (b) was established in blood using an MS9 instrument, and in bone marrow (BM) and spleen (SP) by flow cytometry (n = 10 mice per group). Error bars represent mean ± SD. P-values calculated using Mann-Whitney and reported on the figure panel.

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Supplementary information

Supplementary Information

Flow cytometry gating strategies.

Reporting Summary

Supplementary Table 1

Supplementary Table 1: Clinical annotations of the nine primary samples of patients with AML used in the study; female = 0, male = 1. Supplementary Table 2: sgRNA sequences. Supplementary Table 3: Antibodies used for western blot. Supplementary Table 4: shRNA sequences. Supplementary Table 5: Antibodies used for flow cytometry analyses.

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Unprocessed western blots.

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Kelly, L.M., Rutter, J.C., Lin, K.H. et al. Targeting a lineage-specific PI3Kɣ–Akt signaling module in acute myeloid leukemia using a heterobifunctional degrader molecule. Nat Cancer 5, 1082–1101 (2024). https://doi.org/10.1038/s43018-024-00782-5

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