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PI3Kβ controls immune evasion in PTEN-deficient breast tumours

Nature volume 617, pages 139–146 (2023)Cite this article

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Abstract

Loss of the PTEN tumour suppressor is one of the most common oncogenic drivers across all cancer types1. PTEN is the major negative regulator of PI3K signalling. The PI3Kβ isoform has been shown to play an important role in PTEN-deficient tumours, but the mechanisms underlying the importance of PI3Kβ activity remain elusive. Here, using a syngeneic genetically engineered mouse model of invasive breast cancer driven by ablation of both Pten and Trp53 (which encodes p53), we show that genetic inactivation of PI3Kβ led to a robust anti-tumour immune response that abrogated tumour growth in syngeneic immunocompetent mice, but not in immunodeficient mice. Mechanistically, PI3Kβ inactivation in the PTEN-null setting led to reduced STAT3 signalling and increased the expression of immune stimulatory molecules, thereby promoting anti-tumour immune responses. Pharmacological PI3Kβ inhibition also elicited anti-tumour immunity and synergized with immunotherapy to inhibit tumour growth. Mice with complete responses to the combined treatment displayed immune memory and rejected tumours upon re-challenge. Our findings demonstrate a molecular mechanism linking PTEN loss and STAT3 activation in cancer and suggest that PI3Kβ controls immune escape in PTEN-null tumours, providing a rationale for combining PI3Kβ inhibitors with immunotherapy for the treatment of PTEN-deficient breast cancer.

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Fig. 1: PI3Kβ is required for immune evasion in PTEN- and p53-deficient invasive breast cancer.
Fig. 2: PI3Kβ mediates the formation of an immunosuppressive microenvironment in breast tumours deficient in both PTEN and p53.
Fig. 3: PI3Kβ mediates pro-tumour immune signalling in PTEN-deficient breast tumour cells in a STAT3-dependent manner.
Fig. 4: Identification of biomarkers of resistance to PI3Kβ inhibition-induced immunity in PTEN-deficient PDX models.
Fig. 5: Combined PI3Kβ inhibition and PD-1 blockade synergize to inhibit the growth of breast tumours deficient in PTEN and p53.

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Data availability

Transcriptomic data for bulk RNA-seq and scRNA-seq have been deposited at the Gene Expression Omnibus (GEO) under accession numbers GSE225064 and GSE224906. The scRNA-seq data has also been uploaded to the Single Cell Portal under accession number SCP2118. Source data are provided with this paper.

Code availability

Custom-made R code used for CyCIF analysis is available for download from Zenodo (https://doi.org/10.5281/zenodo.7675466). All R code used to generate figures in the manuscript can be made available upon reasonable request.

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Acknowledgements

The authors thank J. Agudo, R. T. Bronson, L. Ding, M. Kearns, S. Lazo, B. Li, Z. Lin, H. Lu, J. Ni, C. Olson and J. Brugge for experimental assistance and scientific discussions; the laboratories of S. Santagata and P. Sorger and members of the Laboratory of Systems Pharmacology for assistance with tCyCIF; and E. Cahn and the Breast Cancer Advocacy Group (BCAG) at the Dana-Farber/Harvard Cancer Center (DF/HCC) for discussions on patient issues and needs. The authors thank the Dale Family Foundation for their charitable contributions; the DF/HCC Breast SPORE: Specialized Program of Research Excellence (SPORE), an NCI-funded programme, grant 1P50CA168504, for their contributions in the collection and distribution of biospecimens used for establishing PDX models. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health/NCI. This work was supported in part by grants from The Susan G. Komen Foundation PDF16376814 (J.S.B.), Friends of Dana-Farber Cancer Institute (J.S.B.), Cancer Research Institute (Qiwei Wang), Friends of Dana-Farber Cancer Institute (Qiwei Wang), Department of Defense Breast Cancer Research Program Breakthrough Award HT9425-23-1-0026 (Qiwei Wang), Terri Brodeur Breast Cancer Foundation (S.K.), Program Project Grant in Breast and Gynecologic Cancers from DFCI Susan F. Smith Center for Women’s Cancers (G.J.F.), Pew-Stewart Scholars Program for Cancer Research (A.K.S.), Sloan Fellowship in Chemistry (A.K.S.), Breast Cancer Research Foundation (J.J.Z.), and NIH P50 CA101942 (G.J.F.), P50CA206963 (G.J.F.), 1DP2GM119419 (A.K.S.), 1U54CA217377 (A.K.S.), P50 CA168504 (T.M.R. and J.J.Z), CA231945 (T.M.R.) and CA210057 (J.J.Z).

Author information

Author notes

  1. These authors contributed equally: Johann S. Bergholz, Qiwei Wang, Qi Wang

Authors and Affiliations

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

    Johann S. Bergholz, Qiwei Wang, Qi Wang, Weihua Wang, Rong Fang, Sheheryar Kabraji, Kayley Abell-Hart, Shaozhen Xie, Xiaocan Guo, Hao Gu, Thanh Von, Tao Jiang, Shuang Tang, Thomas M. Roberts & Jean J. Zhao

  2. Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA

    Johann S. Bergholz, Qiwei Wang, Shaozhen Xie, Xiaocan Guo, Hao Gu, Shuang Tang, Thomas M. Roberts & Jean J. Zhao

  3. Broad Institute of Harvard and MIT, Cambridge, MA, USA

    Johann S. Bergholz, Qiwei Wang, Michelle Ramseier, Sanjay Prakadan, Alex K. Shalek & Jean J. Zhao

  4. Geode Therapeutics, Inc., Boston, MA, USA

    Qi Wang

  5. Institute for Medical Engineering and Science (IMES), Department of Chemistry, and Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA

    Michelle Ramseier, Sanjay Prakadan & Alex K. Shalek

  6. Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA

    Michelle Ramseier, Sanjay Prakadan & Alex K. Shalek

  7. Ningbo Clinical Pathology Diagnosis Center, Ningbo, P. R. China

    Rong Fang

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

    Sheheryar Kabraji & Gordon J. Freeman

  9. Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA

    Sheheryar Kabraji

  10. Cancer Institute, Fudan University Shanghai Cancer Center, Shanghai, P. R. China

    Qian Zhou & Shuang Tang

  11. Department of Cell Biology and Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA

    G. Kenneth Gray

  12. Department of Cancer Immunology and Virology, Dana-Farber Cancer Institute, Boston, MA, USA

    Hye-Jung Kim

  13. Department of Immunology Discovery, Genentech, South San Francisco, CA, USA

    Hye-Jung Kim

Authors
  1. Johann S. Bergholz
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  2. Qiwei Wang
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Contributions

J.S.B., T.M.R. and J.J.Z. designed and guided the direction of the project. J.S.B., Qiwei Wang and J.J.Z. contributed to the design, analysis and interpretation of all experiments. J.S.B. performed animal dissections, tumour and tissue manipulations and various in vitro experiments, including tissue culturing, drug treatments, virus production and transductions, western blotting, RNA isolation and qPCR, and analysed flow cytometry, CyCIF, bulk RNA-seq and scRNA-seq data. Qiwei Wang designed, performed and analysed flow cytometry and co-culture experiments, and performed tumour and tissue manipulations and various in vitro experiments, including tissue culturing, drug treatments and transfections, CRISPR–Cas 9 editing, western blotting, RNA isolation and qPCR. Qi Wang and J.J.Z. conceived and developed PP, PPA and PPB GEM models of breast tumours and contributed to the design of in vivo efficacy studies. M.R. and S.P. designed, performed, analysed and interpreted scRNA-seq experiments. W.W. and K.A.-H. contributed to the design and performed in vivo drug treatments, performed tumour measurements, contributed to animal health monitoring, performed animal dissections and tumour and tissue manipulations, and performed various in vitro experiments, including tissue culturing, drug treatments, immunohistochemistry, western blotting, RNA isolation, qPCR and ELISA. R.F. contributed to tumour measurements in vivo, and performed numerous in vitro experiments, including cloning, western blotting, RNA isolation, qPCR and ELISA. S.K. designed, performed and analysed CyCIF experiments. Q.Z. and S.T. designed and performed the PPB-mPI3Kβ rescue experiments. G.K.G. designed and performed pilot immune profiling experiments by CyTOF used to guide the design of future immune profiling experiments by flow cytometry. S.X. and H.G. performed and contributed to the analysis of gene expression experiments by RNA-seq. X.G. performed in vitro drug response experiments, western blots, qPCR and ELISA. T.V. and T.J. performed tumour transplantations, in vivo drug treatments and tumour measurements, and contributed to animal health monitoring. G.J.F., H.-J.K. and A.K.S. contributed with key reagents, protocols and scientific discussions. J.S.B. and J.J.Z. wrote the paper. J.S.B., Qiwei Wang, M.R., G.K.G., G.J.F., H.-J.K., A.K.S., T.M.R. and J.J.Z. reviewed and edited the manuscript.

Corresponding authors

Correspondence to Thomas M. Roberts or Jean J. Zhao.

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Competing interests

J.S.B. is a scientific consultant for Geode Therapeutics Inc. J.S.B., G.J.F., T.M.R. and J.J.Z. are co-inventors of DFCI 2180.001 (DFS-166.25) related to this work. Qiwei Wang is a scientific consultant for Crimson Biopharm Inc. Qi Wang is currently an employee at Geode Therapeutics Inc. S.P. is currently an employee at Takeda Pharmaceuticals. S.X. is currently an employee at HiFiBiO Therapeutics. T.V. is currently an employee at MarvelBiome Inc. G.J.F. has patents and pending royalties on the PD-1–PD-L1 pathway from Roche, Merck MSD, Bristol-Myers-Squibb, Merck KGA, Boehringer-Ingelheim, AstraZeneca, Dako, Leica, Mayo Clinic, Eli Lilly and Novartis.  G.J.F. has served on advisory boards for Roche, Bristol-Myers-Squibb, Xios, Origimed, Triursus, iTeos, NextPoint, IgM, Jubilant, Trillium, IOME, Geode, Bright Peak, and GV20. G.J.F. has equity in Nextpoint, Triursus, Xios, iTeos, IgM, Trillium, Invaria, Geode and GV20. H.-J.K. is currently an employee at Genentech. A.K.S. has received compensation for consulting and/or SAB membership from Merck, Honeycomb Biotechnologies, Cellarity, Repertoire Immune Medicines, Hovione, Third Rock Ventures, Ochre Bio, FL82, Empress Therapeutics, Relation Therapeutics, Senda Biosciences, IntrECate biotherapeutics, Santa Ana Bio, and Dahlia Biosciences unrelated to this work. T.M.R. is a SAB member for Shiftbio and K2B Therapeutics and is a co-founder of Geode Therapeutics Inc. J.J.Z. is a co-founder and board director of Crimson Biotech Inc. and Geode Therapeutics Inc. All other authors declare no competing interests.

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Nature thanks Emilio Hirsch, Maurizio Scaltriti and Judith Varner for their contribution to the peer review of this work. Peer review reports are available.

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Extended data figures and tables

Extended Data Fig. 1 Characterization of breast cancer GEM models.

a, H&E staining of primary PP (n = 4), PPA (n = 3) and PPB (n = 4) tumors. Representative images shown. b, Genetic alterations in PTEN and TP53 in patient samples of PAM50 basal breast cancer from The Cancer Genome Atlas (TCGA). c–e, Characterization of PP, PPA and PPB primary tumor cells by western blotting (c) and RNA-Seq (d; n = 5 biological replicates for MMEC; n = 3 biological replicates for PP, PPA and PPB), compared to positive controls for PTEN (MCF7 and HCC1954) and Trp53 (mouse mammary epithelial cells; MMEC). Data in (d) presented as mean values and s.d. e, Tumor cells were treated with 1.0 µM pan-PI3K inhibitor (BKM-120), PI3Kα inhibitor (BYL719) or PI3Kβ inhibitor (AZD6482) for one hour in complete growth media containing 10% FBS. Whole-cell lysates were analyzed by western blotting. Immunoblot representative of two independent experiments. f, Western blot analysis of primary tumor cells. Immunoblot representative of three independent experiments. g, H&E staining of transplanted tumors or remaining tumor bed from nude and FVB mice. Scale bars = 100 μm. Representative images shown (nude; PP, n = 8 tumors; PPA and PPB, n = 4 tumors; FVB, n = 8 tumors per group). h, Tumor-free survival of Rag1+/− (n = 4 mice) and Rag1−/− (n = 8 mice) FVB mice injected with PPB tumor cells. Statistical analysis by Log-rank (Mantel-Cox) test. i–k, PPB tumor cells were stably transfected with a vector control or mouse PI3Kβ. i, Western blot analysis. j, Tumor growth in immunocompetent syngeneic FVB mice (n = 5 tumors per group). Data presented as mean values and s.e.m. k, Tumors after necropsy. l, PPB tumor volume after transplanting into FVB mice treated with or without CD4 or CD8 depleting antibodies (n = 10 tumors per group). Inset shows magnified view of early time points. Data presented as mean values and s.e.m. Apparent molecular weights in kDa are indicated for immunoblots.

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Extended Data Fig. 2 PPB tumor cells exhibit reduced levels of proliferation in vitro and in vivo.

a, Cell proliferation in vitro under reduced serum/nutrient conditions. Data shown as mean and s.d. (n = 6 biological replicates per condition). b–c, Analysis of GFP-tagged PP, PPA and PPB tumors harvested five days after tumor cell transplantation into nude mice. Representative sections (b) and quantification (c) are shown (n = 6 tumors for PP and PPB; n = 3 tumors for PPA). GFP was added via lentiviral transduction in vitro prior to transplantations. Scale bars: 100 μm (left) and 50 μm (right). Box plots represent median and inter-quartile range, and min-to-max error bars (whiskers). For comparison of multiple mean values (a and c), one-way ANOVA followed by Tukey’s multiple comparisons tests.

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Extended Data Fig. 3 Single-cell RNA-Seq analysis of immune infiltrate in PTEN/p53-null breast tumors with catalytic isoform-specific PI3K deletion.

Single-cell RNA-Seq (scRNA-Seq) analysis of immune infiltrate from PP, PPA and PPB tumors (n = 3 mice per group). Tumors were harvested and dissociated into single cells, followed by FACS sorting for viable immune (CD45+) cells prior to scRNA-Seq analysis via Seq-Well technology. a, Heatmap showing gene expression for top 10% differentially-expressed genes (DEGs) for each cell type cluster. Selected genes for each cluster are indicated on the right. Genotype of the tumor of origin is indicated on top. b, tSNE of all CD45+ cells analyzed denoting experimental replicate and cell type. c, Genotype representation per cell type. Complete list of DEGs per cell type is available in the source data linked to this article.

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Extended Data Fig. 4 Single-cell RNA-Seq analysis of monocytes/macrophages cluster in PTEN/p53-null breast tumors with catalytic isoform-specific PI3K deletion.

Single-cell RNA-Seq analysis of monocytes/macrophages (MoMϕ) cluster from PP, PPA and PPB tumors (n = 3 mice per group). a, Heatmap showing gene expression for top 10% differentially-expressed genes (DEGs) for each MoMϕ sub-cluster. Selected genes are indicated on the right. Genotype of the tumor of origin is indicated on top. b, tSNE of all MoMϕ analyzed denoting sub-cluster. c, MoMϕ sub-cluster representation per genotype. d, Genotype representation per MoMϕ sub-cluster. e, GSEA of MoMϕ sub-clusters. Statistical significance of enrichment scores (p-values) were calculated by GSEA using a phenotype-based permutation test. Complete list of DEGs per MoMϕ sub-cluster is available in the source data linked to this article.

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Extended Data Fig. 5 Enhanced immune cell activation in PPB tumors.

a–g, Flow cytometry analysis of PP, PPA and PPB tumors harvested five days after tumor cell transplantation into FVB mice (n = 10 tumors per group). Box plots represent median and inter-quartile range, and min-to-max error bars (whiskers). Representative gating profiles to demonstrate that myeloid-derived IFNɣ is primarily produced by inflammatory monocytes are shown on (f). h, PP, PPA and PPB tumor cells were treated with 100 ng/mL IFNɣ in vitro for 48 h. MHC class I (MHC-I) expression was analyzed by flow cytometry (n = 3 biological replicates per condition). Data shown as median and min-to-max error bars. i, PP, PPA and PPB tumor cells were treated with IFNɣ in vitro for 72 h. Cell viability was analyzed by CellTiter-Glo. Data shown as mean values and s.d. (n = 4 biological replicates per condition). For comparison of two means (h), unpaired, two-tail t-test with Welch’s correction assuming unequal variance. For multiple comparisons (a–e, g and i), one-way ANOVA followed by Tukey’s multiple comparisons tests. Flow cytometry gating profiles available on Supplementary Fig. 1.

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Extended Data Fig. 6 PPB tumor cells enhance immune cell activation ex vivo, and show enhanced inflammatory signaling and reduced STAT3 pathway activation.

a–d, Flow cytometry analyses of immune cells from co-culture experiments ex vivo. a, Analysis of bone marrow-derived cells (BMCs) co-cultured with tumor cells (n = 5 biological replicates per group). b, Analysis of bone marrow-derived dendritic cells (BMDCs) co-cultured with tumor cells (n = 4 biological replicates per group). c, Analysis of CD8+ T-cells co-cultured with tumor cells (n = 4 biological replicates per group). d, Analysis of CD8+ T-cells co-cultured with dendritic cells (DCs) that had previously been co-cultured with tumor cells (n = 4 biological replicates per group). Box plots represent median and inter-quartile range, and min-to-max error bars (whiskers). Flow cytometry gating profiles available on Supplementary Fig. 2. e, Heatmap of gene expression for immune-related genes in primary tumor cells (n = 3 biological replicates per group). f, Analysis by Q-PCR (left) and ELISA (right) of GM-CSF expression by PP and PPB cells. Data presented as mean values and s.d. (n = 3 biological replicates per group). g, Analysis by Q-PCR (left; n = 4 biological replicates per group) and ELISA (right; n = 3 biological replicates per group) of IL-6 expression by PP and PPB cells. Data presented as mean values and s.d. h–i, Immunofluorescence analysis of PP, PPA and PPB tumors harvested five days after tumor cell transplantation into FVB mice, including representative sections (h; n = 3 tumors per group) and quantification of STAT3Y705 phosphorylation in tumor cells as assessed by fluorescence intensity (i). Results correspond to further analysis of samples in Fig. 2a. Tumor cells show positive P-AKT staining (red color). Blue and red-lined arrows point to examples of PP and PPA tumor cells with high P-STAT3. Green-lined arrows point to PPB tumor cell clusters highlighted with white dashed line. For comparison of two means (a–g), unpaired, two-tail t-test with Welch’s correction assuming unequal variance. OD, optical density; TNF, tumor necrosis factor; TLR, toll-like receptor.

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Extended Data Fig. 7 The PI3Kβ-STAT3 pathway mediates immune suppressive signaling in PTEN/p53-null breast tumor cells.

a–d, Analysis of PPB cells stably expressing an empty vector control (vector) or constitutively-active STAT3A661C, N663C (STAT3-CA) by western blotting (a), gene expression (b; n = 3 biological replicates per group), GSEA (c; n = 3 biological replicates per group), and ELISA (d; n = 3 biological replicates per group). e, Flow cytometry analysis of dendritic cells (DCs) co-cultured with tumor cells (n = 4 biological replicates per group). f–i, Analysis of PP cells transduced with shRNA against GFP (shGFP) or Stat3 (shStat3; two independent constructs) by western blotting (f), gene expression (g; n = 3 biological replicates per group), GSEA (h; n = 3 biological replicates per group), and ELISA (i; n = 3 per group). j, Flow cytometry analysis of DCs co-cultured with tumor cells (n = 8 biological replicates per group). k, Tumor volume after transplantation into immunodeficient mice (nude; n = 10 tumors per group) or syngeneic immunocompetent mice (FVB; n = 8 tumors for PP-shGFP and PP-shStat3(1); n = 6 tumors for PP-shStat3(2)). Data presented as mean values and s.e.m. l, Flow cytometry analysis of immune infiltrate after tumor transplantation into FVB mice. Apparent molecular weights in kDa are indicated for immunoblots. Data on (b), (d), (g) and (i) are presented as mean values and s.d. Box plots represent median and inter-quartile range, and min-to-max error bars (whiskers). For comparison of two means (b, d–e), unpaired, two-tail t-test with Welch’s correction assuming unequal variance. For multiple comparisons (g,i,j and l), one-way ANOVA followed by Tukey’s multiple comparisons tests. Flow cytometry gating profiles available on Supplementary Fig. 4. CR, complete regression.

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Extended Data Fig. 8 PI3Kβ regulates STAT3 signaling via TEC Family Kinase BMX.

a, Western blot analysis of PP cells treated with AZD6482 or DMSO as a control in complete growth media or under serum/nutrient starvation conditions. Immunoblot representative of two independent experiments. b–c, Analysis of PP cells treated with AZD6482 under serum/nutrient starvation by gene expression (b; n = 3 biological replicates per condition), and Q-PCR (GM-CSF, n = 3 biological replicates per group; IL-6, n = 4 biological replicates per group) and ELISA (GM-CSF, n = 4 biological replicates per group; IL-6, n = 6 biological replicates per group) (c). For comparison of two means, unpaired, two-tail t-test with Welch’s correction assuming unequal variance. Data in (c) presented as mean values and s.d. d, PP tumor-bearing nude mice were treated with a vehicle control or AZD6482. RNA from isolated tumor cells was analyzed by RNA-Seq. Results from GSEA for AZD6482 compared to vehicle are shown (n = 3 tumors per group). e, Western blot analysis of PTEN-deficient human breast cancer cells treated with AZD6482 or DMSO as a control under reduced nutrient/serum conditions. Immunoblots representative of two biological replicates per condition. f, Immunohistochemistry analysis of HCC70 xenografts from nude mice treated with AZD6482 or a vehicle control. Representative images shown (n = 5 tumors per condition). Scale bars = 100 μm and 50 μm (inset). g–i, Western blot analysis of PP cells treated with the AKT inhibitor MK2206 (g), the TEC Kinase family inhibitor LFM-A13 (h) or the BMX inhibitor BMX-IN-1 (i) under serum/nutrient starvation conditions. Immunoblots representative of two independent experiments performed in duplicate. j, Molecular mechanisms working model. k, Summary of PDX models denoting PTEN genomic status and PTEN protein levels as assessed by IHC. Representative IHC sections are shown (n = 2 tumors per model). Scale bars, 300 μm. Apparent molecular weights in kDa are indicated for immunoblots.

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Extended Data Fig. 9 PD-1 blockade potentiates anti-tumor immune response induced by PI3Kβ inhibition in PTEN-null mouse mammary tumors.

a–b, Flow cytometry analysis of tumor cells and immune infiltrate from PP (a) or HER2/Neu+ (b) tumors harvested from FVB mice treated with AZD6482 or a vehicle control (n = 10 tumors per condition). c, H&E stain of representative tumor samples from mice (Exp. 1) treated with vehicle (n = 8) or combined AZD6482 and PD-1 blockade (αPD-1), including cases showing complete response (CR; n = 3) and delayed progressive disease (PD; n = 3). Scale bars = 100 μm. d–f, Flow cytometry analysis of tumor cells and immune infiltrate from PP tumor-bearing FVB mice treated with AZD6482 or αPD-1 as single agents or in combination (n = 10 tumors per group). Box plots represent median and inter-quartile range, and min-to-max error bars (whiskers). For comparison of two means (a–b), unpaired, two-tail t-test with Welch’s correction assuming unequal variance. For multiple comparisons (d–f), one-way ANOVA followed by Tukey’s multiple comparisons tests. g–h, RNA from bulk PP tumor fragments was analyzed by RNA-Seq (n = 6 per group). Results from GSEA compared to the vehicle control (g) and heatmap denoting expression of immune-related genes (h) are shown. Flow cytometry gating profiles available on Supplementary Figs. 5 and 6. MFI, mean fluorescence intensity; IFN, interferon; NES, normalized enrichment score; PRRs, pattern recognition receptors.

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Extended Data Fig. 10 Mice with complete response to combined PI3Kβ inhibition and PD-1 blockade exhibit robust anti-tumor immunity to tumor re-challenge.

a–c, Flow cytometry analysis of tumor immune infiltrate (a; n = 12 tumors for naïve; n = 10 tumors for re-challenged), draining lymph nodes (b; n = 12 lymph nodes for naïve; n = 10 lymph nodes for re-challenged) and spleens (c; n = 6 spleens for naïve; n = 5 spleens for re-challenged) from age-matched FVB mice challenged for the first time (naïve; n = 6 mice) and in mice with complete response to combined treatment and challenged for a second time (re-challenged; n = 5 mice). Tissues were harvested two weeks after re-challenging. Box plots represent median and inter-quartile range, and min-to-max error bars (whiskers). For comparison of two means, unpaired, two-tail t-test with Welch’s correction assuming unequal variance. Flow cytometry gating profiles available on Supplementary Fig. 7. MFI, mean fluorescence intensity; Tregs, regulatory T-cells.

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Extended Data Fig. 11 PTEN loss sensitizes tumors to enhancement of anti-tumor immune response by pharmacological PI3Kβ-specific inhibition.

a, Western blot analysis of RCT-E302 mouse mammary tumor cells with deleted Pten (RCT-E302-sgPten) and parental RCT-E302 cells. b–c, Flow cytometry analysis of tumor cells and immune infiltrate from RCT-E302-sgPten (b) or RCT-E302 (c) tumors harvested from FVB mice treated with AZD6482 or a vehicle control (n = 10 tumors per group). d, Western blot analysis of BPP (Brca1/Trp53/Pten triple-null) and BP (Brca1/Trp53 double-null) mouse mammary tumor cells. e–f, Flow cytometry analysis of tumor cells and immune infiltrate from BPP (e) or BP (f) tumors harvested from FVB mice treated with AZD6482 or a vehicle control (n = 10 tumors per group). Apparent molecular weights in kDa are indicated for immunoblots. For comparison of two means (b–c and e–f), unpaired, two-tail t-test with Welch’s correction assuming unequal variance. Flow cytometry gating profiles available on Supplementary Fig. 8.

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Extended Data Fig. 12 Combined PI3Kβ inhibition and immunotherapy inhibit tumor growth in mouse models of PTEN-null breast cancer.

a–b, Tumor-bearing mice were treated with AZD6482 or a monoclonal antibody against mouse PD-1 (αPD-1) alone or in combination, as shown. Tumor growth curves (left panels) and tumor growth inhibition (TGI; right panels) are shown (n = 8 tumors for AZD6482 in (a); n = 10 tumors for other conditions in (a) and (b)). c, Western blot analysis of 4T1 mouse mammary tumor cells with deleted Pten (4T1-sgPten) and parental 4T1 cells. Image is representative of two independent immunoblots. Apparent molecular weights in kDa are indicated. d, Ratio of M2-like polarized (CD206High MHC-II-) to M1-like polarized (CD206- MHC-IIHigh) macrophages (CD11b+ F4/80+) as determined by flow cytometry (n = 10 tumors per group). e, 4T1-sgPten tumor-bearing mice were treated with AZD6482, αPD-1 or the STING agonist MSA2 alone or in combination. Tumor growth curves (left panels) and TGI (right panels) are each shown as two separate plots for clarity, with all plots including the same vehicle and AZD6482 groups for easier comparison (n = 8 tumors for MSA; n = 10 tumors for other conditions). Data for tumor growth curves presented as mean values and s.e.m. Box plots represent median and inter-quartile range, and min-to-max error bars (whiskers). For comparison of multiple mean values (a–b and d–e), one-way ANOVA followed by Tukey’s multiple comparisons tests. Statistical analysis on (e) was performed including all groups in the comparisons, with results shown in two different graphs for easier visual assessment. Flow cytometry gating profiles are available on Supplementary Fig. 9.

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Supplementary Tables 1–14

Supplementary Tables 15–17

Tables listing antibodies used for CyCIF, flow cytometry and western blots.

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Bergholz, J.S., Wang, Q., Wang, Q. et al. PI3Kβ controls immune evasion in PTEN-deficient breast tumours. Nature 617, 139–146 (2023). https://doi.org/10.1038/s41586-023-05940-w

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