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Targetable leukaemia dependency on noncanonical PI3Kγ signalling

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Abstract

Phosphoinositide-3-kinase-γ (PI3Kγ) is implicated as a target to repolarize tumour-associated macrophages and promote antitumour immune responses in solid cancers1,2,3,4. However, cancer cell-intrinsic roles of PI3Kγ are unclear. Here, by integrating unbiased genome-wide CRISPR interference screening with functional analyses across acute leukaemias, we define a selective dependency on the PI3Kγ complex in a high-risk subset that includes myeloid, lymphoid and dendritic lineages. This dependency is characterized by innate inflammatory signalling and activation of phosphoinositide 3-kinase regulatory subunit 5 (PIK3R5), which encodes a regulatory subunit of PI3Kγ5 and stabilizes the active enzymatic complex. We identify p21 (RAC1)-activated kinase 1 (PAK1) as a noncanonical substrate of PI3Kγ that mediates this cell-intrinsic dependency and find that dephosphorylation of PAK1 by PI3Kγ inhibition impairs mitochondrial oxidative phosphorylation. Treatment with the selective PI3Kγ inhibitor eganelisib is effective in leukaemias with activated PIK3R5. In addition, the combination of eganelisib and cytarabine prolongs survival over either agent alone, even in patient-derived leukaemia xenografts with low baseline PIK3R5 expression, as residual leukaemia cells after cytarabine treatment have elevated G protein-coupled purinergic receptor activity and PAK1 phosphorylation. Together, our study reveals a targetable dependency on PI3Kγ–PAK1 signalling that is amenable to near-term evaluation in patients with acute leukaemia.

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Fig. 1: Genome-wide CRISPRi screening identifies a selective dependency on PI3Kγ in a subset of leukaemias.
Fig. 2: Selective dependency on PI3Kγ subunits is shared by a leukaemia subset that is defined by innate inflammatory signalling and activated PIK3R5.
Fig. 3: PI3Kγ inhibition suppresses OXPHOS in leukaemia cells.
Fig. 4: The intrinsic dependency of leukaemia cells on the PI3Kγ complex is reliant on PAK1 but not AKT.
Fig. 5: Eganelisib synergizes with cytarabine in leukaemia treatment.

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Data availability

The RNA-seq data are available at the Gene Expression Omnibus under accession numbers GSE243654 (PI3Kγ inhibition in CAL-1 cells) and GSE243669 (cytarabine-persistent leukaemias). The ATAC-seq data are available at the Gene Expression Omnibus under accession number GSE243666. The mass spectrometry data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository60 with the dataset identifier PXD045702. TCGA AML data were obtained from the National Cancer Institute Genomic Data Commons: https://portal.gdc.cancer.gov. Source data are provided with this paper.

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Acknowledgements

Q.L. is a Fellow of The Leukemia & Lymphoma Society. Work by M.A.P. is funded by Instituto de Salud Carlos III (ISCIII) and co-funded by the European Union, through the project ‘CP21/00017’. X.W. is supported by a National Cancer Center fellowship grant. C.A.G.B. is a Special Fellow of The Leukemia & Lymphoma Society. R.X. is a Dr Oliver Press Memorial Fellow of Lymphoma Research Foundation. P.v.G. and A.A.L. are supported by the Ludwig Center at Harvard. A.A.L. is a Scholar of The Leukemia & Lymphoma Society. A.A.L. is supported by the National Cancer Institute (R37 CA225191), US Department of Defense (W81XWH-20-1-0683), the Mark Foundation for Cancer Research and the Bertarelli Rare Cancers Fund. None of the funding bodies was directly involved in the design of the study, nor in the collection, analysis or interpretation of the data, or writing of the manuscript.

Author information

Authors and Affiliations

  1. Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA

    Qingyu Luo, Evangeline G. Raulston, Karley S. Whalen, Kezhi Yan, Christopher A. G. Booth, Ran Xu, Shai Shimony, Henry W. Long & Andrew A. Lane

  2. Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Oviedo, Spain

    Miguel A. Prado

  3. Department of Cell Biology, Harvard Medical School, Boston, MA, USA

    Miguel A. Prado & Joao A. Paulo

  4. Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA

    Xiaowei Wu

  5. Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA

    Xiaowei Wu

  6. Department of Medical Oncology, Albert Einstein College of Medicine, Bronx, NY, USA

    Kira Gritsman

  7. Department of Cell Biology, Albert Einstein College of Medicine, Bronx, NY, USA

    Kira Gritsman

  8. Division of Hematology, Brigham and Women’s Hospital, Boston, MA, USA

    Peter van Galen

  9. Genetic Perturbation Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    John G. Doench

  10. Department of Hematology, Rabin Medical Center, Tel Aviv Faculty of Medicine, Tel Aviv, Israel

    Shai Shimony

  11. Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA

    Henry W. Long

  12. Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA

    Donna S. Neuberg

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Contributions

Q.L., E.G.R., X.W., K.S.W., K.Y., C.A.G.B. and R.X. conducted most of the experiments and analysed data. E.G.R., X.W., K.G., P.v.G., J.G.D. and D.S.N. assisted in experimental design and data interpretation. M.A.P. and J.A.P. performed the proteomic and phosphoproteomic experiments and data analysis. S.S. conducted clinical data analysis. H.W.L. performed the ATAC-seq analysis. Q.L. and A.A.L. designed the study, interpreted the data and wrote the paper. A.A.L. supervised the study. All authors read and participated in editing the paper.

Corresponding author

Correspondence to Andrew A. Lane.

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Competing interests

K.G. received research funding from iOnctura and ADC Therapeutics. H.W.L. received research funding from Novartis. A.A.L. received research funding from AbbVie and Stemline Therapeutics; consulting fees from Cimeio Therapeutics, IDRx, Jnana Therapeutics, ProteinQure and Qiagen; and has equity as an advisor for Medzown. All other authors declare no competing interests.

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Extended data figures and tables

Extended Data Fig. 1 PIK3R5 expression is significantly elevated in a leukemia subset including BPDCN.

a, Overlapping gene numbers between the day 21 dependencies and common essential as well as nonessential genes from DepMap. b, Significantly enriched pathways with the Elsevier Pathway Collection via Enrichr analyses of the 184 specific core dependencies or the 143 common essentials. P values were adjusted for multiple comparisons. c, Flowchart showing the bioinformatic screening strategy to identify potential specific targets in a leukemia subset including BPDCN. d, Expression of the 16 genes that are dependencies in <70% of AML cell lines from panel c in various blood cancer subtypes. P values with two-sided t test are shown, without adjustment for multiple comparisons. e, Expression of the 16 genes that are dependent in <70% of AML cell lines from panel c in normal pDC and BPDCN. P values with two-sided t test are shown, without adjustment for multiple comparisons.

Extended Data Fig. 2 Newly established BPDCN cell lines maintain features of patient samples.

a, DepMap gene effects of PIK3R5 and PIK3CG in all cancer types. b, Flowchart showing the strategy of using leukemia adaptive medium (LAM) to establish new BPDCN cell lines. c, Representative images and tumor volume of the intradermal tumors formed by DF-DCN-86 in NSG mice. n = 2, biological replicates. d, Hematoxylin and eosin staining of the tissue sections from intradermal tumors formed by DF-DCN-86. Scale bar, 50 μm. Representative results from eight biological replicates are shown. e, RT-qPCR of PI3K subunits mRNA in leukemia cell lines. Data are means ± S.D.; n = 3, technical replicates. Representative results from three biological replicates are shown. f, Western blotting for PI3K subunits in leukemia cell lines. Representative results from three biological replicates are shown. For gel source data, see Supplementary Fig. 1.

Extended Data Fig. 3 Most BPDCN and some AML cell lines are sensitive to PI3Kγ inhibition.

a, Western blotting for PIK3R5 and PIK3CG in various leukemia cell lines after depletion of PIK3R5 or PIK3CG. Representative results from three biological replicates are shown. b, Relative cell numbers of various leukemia cell lines after 2, 4, and 6 days of treatment with indicated PI3K inhibitors (each at 1 μM) as compared to control cells. Data are means ± S.E.M.; n = 3, biological replicates. c, EdU positive cell percentages of leukemia cell lines treated with vehicle control (DMSO) or eganelisib (1 μM) for 48 h. Data are means ± S.E.M.; n = 3, biological replicates. d, Relative cell numbers of various leukemia cell lines after 2, 4, and 6 days of treatment with indicated PI3K inhibitors (each at 1 μM) as compared to control cells. Data are means ± S.E.M.; n = 3, biological replicates. Based on the sensitivity to eganelisib: strongly sensitive cell lines are labeled in red, moderately sensitive cell lines are labeled in blue, and insensitive cell lines are labeled in grey. For gel source data, see Supplementary Fig. 1. For gating strategy, see Supplementary Fig. 2.

Extended Data Fig. 4 PIK3R5 can be activated by innate inflammatory signaling.

a, RT-qPCR of PIK3R5 mRNA in leukemia PDXs. Data are means ± S.D.; n = 3, technical replicates. Representative results from three biological replicates are shown. b, Inhibition curve of leukemia PDXs after ex vivo treatment with increasing doses of eganelisib (0-4 μM) for 72 h. Data are means ± S.D.; n = 3, technical replicates. Representative results from three biological replicates are shown. c, Relative caspase-3 and 7 activities in leukemia PDXs after ex vivo treatment with eganelisib (1 μM) for 72 h as compared to vehicle control (DMSO). Data are means ± S.E.M.; PIK3R5-high, n = 10; PIK3R5-low, n = 6. Biological replicates. P value with two-sided Mann-Whitney test is shown, without adjustment for multiple comparisons. d, Flow cytometry of CD11b and CD14 in leukemia PDXs after ex vivo treatment with eganelisib (1 μM) or vehicle control (DMSO) for 72 h. Representative results from three biological replicates are shown. e, Comutation plot showing gene mutations of TCGA AML cases with high or low PIK3R5 expression. f, Overall survival of AML patients from TCGA. P values with Log-rank test are shown, without adjustment for multiple comparisons. g, Heatmap showing expression scores of IIRS genes. The IIRS score was determined by first transforming the relative expression of each gene to values resulting in a mean of 0 and S.D. of 1 across all patients, then summing up individual gene scores to obtain the overall score. h, Overall survival of AML patients from TCGA. P values with Log-rank test are shown, without adjustment for multiple comparisons. i, Proportions of FAB subtypes of AML patients from TCGA. j, RT-qPCR of PIK3R5 and PIK3CG mRNA in leukemia cell lines after resiquimod treatment for 48 h. Data are means ± S.D.; n = 3, technical replicates. Representative results from three biological replicates are shown. k, Relative viability of leukemia PDXs after ex vivo treatment with increasing doses of eganelisib (0-4 μM) for 72 h, with or without resiquimod (1 μg/ml). Data are means ± S.D.; n = 3, technical replicates. Representative results from three biological replicates are shown. For gating strategy, see Supplementary Fig. 2.

Extended Data Fig. 5 PIK3R5 is transcriptionally activated by SPI1 and stabilizes the PI3Kγ complex.

a, ATAC-seq peaks at PIK3R5 and PIK3CG genomic regions. b, Schematic showing the overlapping areas between PIK3R5 promoter regions and transcriptional regulators from ChIP-seq data in the ENCODE database. Each gene’s log2 fold change and -log10 P value from the genome-wide CRISPRi screen (Fig. 1b; day 21 vs day 0) are shown on the right side. c,d, RT-qPCR (c) and western blotting (d) analyses of PIK3R5 and PIK3CG expressions in CAL-1 cells with nontargeting (shControl), SPI1 targeting (shSHP1#1, shSPI1#2), or ASH2L targeting (shASH2L#1, shASH2L#2) shRNAs. Data are means ± S.D.; n = 3, technical replicates. Representative results from three biological replicates are shown. e, ChIP detecting the enrichment of RNA polymerase II (Pol II) and SPI1 on PIK3R5 promoter regions in THP-1 cells treated with vehicle (DMSO) or resiquimod (1 μg/ml) for 48 h. Data are means ± S.D.; n = 3, technical replicates. Representative results from three biological replicates are shown. f, RT-qPCR of PIK3R5 mRNA in THP-1 cells expressing nontargeting, SPI1 targeting, or ASH2L targeting shRNAs after resiquimod treatment (1 μg/ml) for 48 h. Data are means ± S.D.; n = 3, technical replicates. Representative results from three biological replicates are shown. g, RT-qPCR of PIK3R5 and PIK3CG mRNA in CAL-1 cells expressing nontargeting control (NTC#1 and NTC#2), or PIK3R5 or PIK3CG targeting CRISPRi guide RNAs (PIK3R5#1, PIK3R5#2, PIK3CG#1, and PIK3CG#2). Data are means ± S.D.; n = 3, technical replicates. Representative results from three biological replicates are shown. h, Correlation analyses of PIK3R5 and PIK3CG mRNA or protein expressions in leukemia cell lines. R and two-sided P values with Spearman correlation coefficients are shown, without adjustment for multiple comparisons. i, RT-qPCR of PIK3CG mRNA in leukemia PDXs. Data are means ± S.D.; n = 3, technical replicates. Representative results from three biological replicates are shown. j, Western blotting for PIK3R5 and PIK3CG in various leukemia PDX samples. Representative results from three biological replicates are shown. k, Correlation analyses of PIK3R5 and PIK3CG mRNA or protein expressions in leukemia PDXs. R and two-sided P values with Spearman correlation coefficients are shown, without adjustment for multiple comparisons. For gel source data, see Supplementary Fig. 1.

Extended Data Fig. 6 PI3Kγ inhibition activates an NFκB-mediated transcriptional network of tumor suppressor genes.

a, Seahorse assays measuring glycolysis in the indicated leukemia cell lines upon treatment with eganelisib or LY294002 (each at 1 μM for 48 h). Data are means ± S.D.; n = 5, biological replicates. b, Seahorse assays measuring glycolysis in control, PIK3R5 depleted, or PIK3CG depleted CAL-1 cells. Data are means ± S.D.; n = 5, biological replicates. c, Relative numbers of CAL-1 cells after treatment with palbociclib (250 nM) for 2, 4, and 6 days. Data are means ± S.E.M.; n = 3, biological replicates. d, Seahorse assays detecting mitochondrial respiration in CAL-1 cells upon treatment with palbociclib (250 nM) for 48 h. Data are means ± S.D.; n = 5, biological replicates. e, GSEA results indicating TNFα signaling via NFκB to be the most activated gene set upon eganelisib treatment or PIK3R5 depletion in CAL-1 cells. P values were not adjusted for multiple comparisons. f, Significantly upregulated or downregulated genes in CAL-1 cells treated with eganelisib or depleted of PIK3R5. Five genes belonging to the TNFα signaling via NFκB gene set are labelled in red. g, Relative numbers of CAL-1 cells expressing nontargeting control or gene-targeting CRISPRi guide RNAs. Data are means ± S.E.M.; n = 3, biological replicates. h, Western blotting for the indicated proteins in CAL-1 cells after depletion of each PI3K subunit individually by CRISPRi. Representative results from three biological replicates are shown. i, RT-qPCR of gene expression in leukemia cells upon treatment with eganelisib or LY294002 (each at 1 μM for 48 h). Data are means ± S.D.; n = 3, technical replicates. Representative results from three biological replicates are shown. For gel source data, see Supplementary Fig. 1.

Extended Data Fig. 7 NFκB inhibition reduces leukemia dependency on PI3Kγ.

a, RT-qPCR of gene expression in CAL-1 cells expressing nontargeting control or gene-targeting CRISPRi guide RNAs. Data are means ± S.D.; n = 3, technical replicates. Representative results from three biological replicates are shown. b, Relative numbers of CAL-1 cells expressing nontargeting control or gene-targeting CRISPRi guide RNAs. Eganelisib: 1 μM. Data are means ± S.E.M.; n = 3, biological replicates. c, Western blotting for p65 phosphorylation in CAL-1 cells treated with eganelisib (1 μM), with or without NFκB inhibitors IKK-16 (0.5 μM) or SC-514 (20 μM), for 48 h. Representative results from three biological replicates are shown. d, RT-qPCR detection of gene expression in CAL-1 cells treated with eganelisib (1 μM), with or without NFκB inhibitors IKK-16 (0.5 μM) or SC-514 (20 μM), for 48 h. Data are means ± S.D.; n = 3, technical replicates. Representative results from three biological replicates are shown. e,f, Relative numbers (e) and viability (f) of CAL-1 cells after treatment with eganelisib (1 μM), with or without NFκB inhibitors IKK-16 (0.5 μM) or SC-514 (20 μM), for 2, 4, and 6 days. Data are means ± S.E.M.; n = 3, biological replicates. g, Seahorse assays detecting mitochondrial respiration in CAL-1 cells treated with eganelisib (1 μM), with or without NFκB inhibitors IKK-16 (0.5 μM) or SC-514 (20 μM), for 48 h. Data are means ± S.D.; n = 5, biological replicates. h, Relative numbers of CAL-1 cells after treatment with IACS-010759 (1 μM), with or without NFκB inhibitors IKK-16 (0.5 μM) or SC-514 (20 μM), for 2, 4, and 6 days. Data are means ± S.E.M.; n = 3, biological replicates. i, Seahorse assays detecting mitochondrial respiration in CAL-1 cells upon treatment with IACS-010759 (1 μM), with or without NFκB inhibitors IKK-16 (0.5 μM) or SC-514 (20 μM), for 48 h. Data are means ± S.D.; n = 5, biological replicates. For gel source data, see Supplementary Fig. 1.

Extended Data Fig. 8 PAK1 phosphorylation determines leukemia dependency on PI3Kγ.

a, GSEA indicating that the PI3K-AKT-mTOR signaling pathway downstream genes were not significantly affected upon eganelisib treatment (1 μM for 48 h) or PIK3R5 depletion (PIK3R5i) in CAL-1 cells. P values were not adjusted for multiple comparisons. b, Volcano plot showing the upregulated or downregulated proteins by mass spectrometry in CAL-1 cells treated with eganelisib or depleted of PIK3R5 (PIK3R5i). PIK3R5 (two dots represent different PIK3R5 Uniprot protein IDs Q8WYR1 and J3KSW1, respectively) occurred as the most significantly downregulated protein, confirming the knockdown efficiency of PIK3R5. PIK3CG protein level was also moderately decreased due to its reduced stability after PIK3R5 depletion. P values were adjusted for multiple comparisons. c, Western blotting for the indicated proteins in CAL-1 cells after treatment with eganelisib (1 μM) for 0.5, 3, 6, and 24 h. Representative results from three biological replicates are shown. d, Western blotting for the indicated proteins in AML PDXs after ex vivo treatment with eganelisib (1 μM) for 30 min. Representative results from three biological replicates are shown. e, Western blotting for active Rac1 or Cdc42 in CAL-1 cells treated with vehicle control (DMSO) or eganelisib (1 μM) for 30 min. Representative results from three biological replicates are shown. f, Inhibition curve of bone marrow normal CD34+ cells and AML patient samples after ex vivo treatment with increasing doses of eganelisib (0-4 μM) for 72 h. Data are means ± S.D.; n = 3, technical replicates. Representative results from three biological replicates are shown. g, Western blotting for the indicated proteins in bone marrow normal CD34+ cells and AML patient samples. h, Western blotting for Flag and PAK1 in CAL-1 cells expressing empty vector (EV) or Flag-tagged PAK1 mutants. Representative results from three biological replicates are shown. i, Relative cell numbers of CAL-1 cells expressing empty vector (EV) or PAK1 mutants after treatment with vehicle control (DMSO) or eganelisib (1 μM) for 2, 4, and 6 days. Data are means ± S.E.M.; n = 3, biological replicates. j, Seahorse assays detecting mitochondrial respiration in CAL-1 cells expressing empty vector (EV) or PAK1 mutants after treatment with vehicle control (DMSO) or eganelisib (1 μM) for 48 h. Data are means ± S.D.; n = 5, biological replicates. k, RT-qPCR detection of gene expressions in CAL-1 cells expressing empty vector (EV) or PAK1 mutants after treatment with vehicle control (DMSO) or eganelisib (1 μM) for 48 h. Data are means ± S.D.; n = 3, technical replicates. Representative results from three biological replicates are shown. For gel source data, see Supplementary Fig. 1.

Extended Data Fig. 9 Eganelisib is well tolerated in vivo and synergizes with cytarabine in leukemia treatment.

a, Heatmaps showing inhibition percentages and synergy scores of combined eganelisib and cytarabine treatment in CAL-1 and DF-DCN-86 cells. b, Western blotting for PAK1 phosphorylation in DF-DCN-86 intradermal xenografts. The standard sample was created by mixing an equal amount from each of the 5 control samples. c. Survival of mice xenografted with DF-DCN-86 and treated with vehicle control or eganelisib. n = 5, biological replicates. P values with Log-rank test are shown, without adjustment for multiple comparisons. d, Body weight of mice xenografted with DF-DCN-86 and treated with vehicle control or eganelisib. Data are means ± S.E.M.; n = 5, biological replicates. e, Complete blood count of mice xenografted with DF-DCN-86 and treated with vehicle control or eganelisib. WBC, white blood cells, K/μL; NE, neutrophils, K/μL; LY, lymphocytes, K/μL; MO, monocytes, K/μL; EO, eosinophils, K/μL; BA, basophils, K/μL; RBC, red blood cells, M/μL; Hb, hemoglobin, g/dL; HCT, hematocrit test, %; MCV, mean corpuscular volume, fL; MCH, mean corpuscular hemoglobin, pg; MCHC, mean corpuscular hemoglobin concentration, g/dL; RDW, red blood cell distribution width, %; PLT, platelet count, K/μL. Data are means ± S.E.M.; n = 5, biological replicates. For gel source data, see Supplementary Fig. 1.

Extended Data Fig. 10 Cytarabine-persistent leukemias show elevated G protein-coupled purinergic receptor signaling and PAK1 phosphorylation.

a, Inhibition curve of leukemia PDXs after ex vivo treatment with increasing doses of cytarabine (0-320 μM) for 72 h. Data are means ± S.D.; n = 3, technical replicates. Representative results from three biological replicates are shown. b, Western blotting for the indicated proteins in untreated or cytarabine-persistent PDX cells (BPDCN01: 320 μM; AML20: 5 μM; B-ALL106: 10 μM; each for 72 h). Representative results from three biological replicates are shown. c, Schematic showing the strategy for establishing a cytarabine-persistent PDX model using AML20. d, Flow cytometry for human CD45+ cell percentage in bone marrow samples from untreated or cytarabine-treated mice. Data are means ± S.E.M.; n = 5, biological replicates. e, GSEA indicating G protein-coupled purinergic receptor signaling pathways are significantly elevated in cytarabine-persistent AML20 cells. P values were not adjusted for multiple comparisons. f, GSEA indicating that neither the PI3K-AKT-mTOR signaling pathway nor non-purinergic G protein-coupled receptor signaling pathways were significantly elevated in cytarabine-persistent AML20 cells. P values were not adjusted for multiple comparisons. g, RT-qPCR of gene expression in U937 cells expressing nontargeting control (NTC) or gene targeting CRISPRa guide RNAs. Data are means ± S.D.; n = 3, technical replicates. Representative results from three biological replicates are shown. h, Western blotting for the indicated proteins in U937 cells upon CRISPRa-mediated activation of each G protein-coupled purinergic receptor individually. Representative results from three biological replicates are shown. i, Proposed model illustrating leukemia dependency on noncanonical PI3Kγ signaling. The PI3Kγ complex can be activated via two mechanisms: 1. Intrinsically (baseline inflammatory signature) or extrinsically (via TLR agonists or cytokines) activated innate inflammatory signaling promotes high PIK3R5 expression and subsequent stabilization of the PI3Kγ complex; 2. Leukemias without baseline elevated PIK3R5 acquire increased PI3Kγ activity through G protein-coupled purinergic receptor signaling after cytarabine treatment. In either circumstance, PI3Kγ activation leads to increased PAK1 phosphorylation, which drives leukemia via suppression of an NFκB-mediated transcriptional network of tumor suppressor genes. Inhibition of PI3Kγ reactivates the NFκB-related tumor suppressor genes, leading to compromised OXPHOS and leukemia cell death. For gel source data, see Supplementary Fig. 1. For gating strategy, see Supplementary Fig. 2.

Supplementary information

Supplementary Figures

This file contains Supplementary Figs. 1 and 2

Reporting Summary

Supplementary Table 1

Genomic profiling of BPDCN cell lines. STR profiling and DNA mutations for CAL-1, DF-DCN-86, DF-DCN-1, and DF-DCN-2.

Supplementary Table 2

Sequences of CRISPRi, CRISPRa, shRNA, and cDNA. CRISPRi guide RNA sequences for nontargeting controls, PIK3R5, PIK3CG, PIK3CA, PIK3CB, PIK3CD, RHOB, EGR1, CD69, SGK1, and EGR3. CRISPRa guide RNA sequences for nontargeting control, ADORA2A, ADORA2B, ADORA3, P2RY6, P2RY12, and P2RY13. shRNA sequences for control, PIK3R5, PIK3CG, SPI1, and ASH2L. cDNA sequences for PAK1 mutants.

Supplementary Table 3

Information for primary patient samples. Source and pathologic characteristics of AML patient samples.

Supplementary Table 4

Fold change of all gene targets in CRISPRi screening. Fold change and P value of all gene targets at day 14 and day 21 in CRISPRi screening.

Supplementary Table 5

Mass spectrometry parameters. MS parameters for proteomics and phosphoproteomics.

Supplementary Table 6

Protein and phosphoprotein quantification. Protein and phosphoprotein quantification from mass spectrometry.

Source data

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Luo, Q., Raulston, E.G., Prado, M.A. et al. Targetable leukaemia dependency on noncanonical PI3Kγ signalling. Nature (2024). https://doi.org/10.1038/s41586-024-07410-3

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