Abstract
Genes that drive the proliferation, survival, invasion and metastasis of malignant cells have been identified for many human cancers1,2,3,4. Independent studies have identified cell death pathways that eliminate cells for the good of the organism5,6. The coexistence of cell death pathways with driver mutations suggests that the cancer driver could be rewired to activate cell death using chemical inducers of proximity (CIPs). Here we describe a new class of molecules called transcriptional/epigenetic CIPs (TCIPs) that recruit the endogenous cancer driver, or a downstream transcription factor, to the promoters of cell death genes, thereby activating their expression. We focused on diffuse large B cell lymphoma, in which the transcription factor B cell lymphoma 6 (BCL6) is deregulated7. BCL6 binds to the promoters of cell death genes and epigenetically suppresses their expression8. We produced TCIPs by covalently linking small molecules that bind BCL6 to those that bind to transcriptional activators that contribute to the oncogenic program, such as BRD4. The most potent molecule, TCIP1, increases binding of BRD4 by 50% over genomic BCL6-binding sites to produce transcriptional elongation at pro-apoptotic target genes within 15 min, while reducing binding of BRD4 over enhancers by only 10%, reflecting a gain-of-function mechanism. TCIP1 kills diffuse large B cell lymphoma cell lines, including chemotherapy-resistant, TP53-mutant lines, at EC50 of 1–10 nM in 72 h and exhibits cell-specific and tissue-specific effects, capturing the combinatorial specificity inherent to transcription. The TCIP concept also has therapeutic applications in regulating the expression of genes for regenerative medicine and developmental disorders.
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Data availability
Uncropped blots of western blots and Coomassie gels of recombinant proteins are available in Supplementary Fig. 1a,b, respectively. The flow gating strategy is available in Supplementary Fig. 2. Select gene expression changes in tissue from mice treated with TCIP1 are annotated in Supplementary Table 1. Source data for mouse drug levels in plasma and tissue and for body weight changes are provided. Sequencing data have been deposited to GSE211282. Source data are provided with this paper.
Change history
18 August 2023
A Correction to this paper has been published: https://doi.org/10.1038/s41586-023-06543-1
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Acknowledgements
The studies described in this article were funded from a grant from the HHMI to G.R.C. and NIH grants CA276167, CA163915 and MH126720-01 to G.R.C. Funding was also provided by a grant from the Mary Kay Foundation. Funding was provided to S.G. from the NIH grant 5F31HD103339-03. G.R.C., S.G., A.K., S.H.K., C.-Y.C. and J.M.S. were mentored and financially supported by Stanford’s SPARK Translational Research Program. G.R.C. was supported by the David Korn Professorship. This research was financially supported by Stanford Bio-X. Funding was provided to N.S.G. from departmental funds from Chemical and Systems Biology and the Stanford Cancer Institute, and the Gray laboratory also receives or has received research funding from Novartis, Takeda, Astellas, Taiho, Jansen, Kinogen, Arbella, Deerfield, Springworks, Interline and Sanofi. Funding for pharmacokinetic studies was provided by NIH grant number 1 S10OD030332-01. M.R.G. is supported by a Leukemia and Lymphoma Society Scholar award. S.G. thanks T. Reindl, E. Bruguera and S. Hinshaw for helpful advice for the biochemical studies. We thank I. A. Graef for thoughtful comments on the manuscript, and members of the Crabtree and Gray laboratories for constructive comments.
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Authors and Affiliations
Contributions
G.R.C. conceived the TCIP strategy to regulate endogenous genes and the rewiring of cancer drivers to activate cell death pathways. S.G. contributed many inventive ideas, designed the first effective TCIP and performed the biochemical, cell biological and genomic studies. A.K. contributed many inventive ideas, defined the conditions for TCIP use, carried out the first successful TCIP experiments and conducted the cell biological and genomic studies. C.-Y.C. suggested the use of BCL6 as a means of producing cell death. S.H.K. screened the cell death genes to detect those that would directly kill cancer cells. N.S.G. and S.G. designed the first effective TCIPs. X.L. synthesized the first effective TCIP. Z.L. and T.Z. contributed to TCIP design and optimization. W.J. synthesized TCIP1, the most potent TCIP to date. W.W. contributed innovative ideas to the computational analysis for the selection of TCIP components and helped fund the studies by writing grants with G.R.C. J.M.S. carried out experiments designed by G.R.C., S.G. and A.K. H.V. conducted the histopathology studies. H.Y. and M.R.G. contributed to TCIP1 application in DLBCL. The manuscript was written by G.R.C., S.G., A.K. and N.S.G. with input from all authors.
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Competing interests
G.R.C. is a founder and scientific advisor for Foghorn Therapeutics and Shenandoah Therapeutics. N.S.G. is a founder, science advisory board member (SAB) and equity holder in Syros, C4, Allorion, Lighthorse, Voronoi, Inception, Matchpoint, CobroVentures, GSK, Shenandoah (board member), Larkspur (board member) and Soltego (board member). The Gray laboratory receives or has received research funding from Novartis, Takeda, Astellas, Taiho, Jansen, Kinogen, Arbella, Deerfield, Springworks, Interline and Sanofi. T.Z. is a scientific founder, equity holder and consultant of Matchpoint, equity holder of Shenandoah, and consultant of Lighthorse. M.R.G. reports research funding from Sanofi, Kite/Gilead, Abbvie and Allogene; consulting for Abbvie, Allogene and Bristol Myers Squibb; honoraria from Tessa Therapeutics, Monte Rosa Therapeutics and Daiichi Sankyo; and stock ownership of KDAc Therapeutics. Shenandoah has a license from Stanford for the TCIP technology that was invented by G.R.C., S.G., A.K., C-Y.C, W.W., S.H.K., N.S.G., W.J., X.L. and Z.L. All other authors declare no competing interests.
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Extended data figures and tables
Extended Data Fig. 1 Potency of TCIP1 in cancer cell lines and correlation with BCL6 level.
a. Comparison of TCIP1 effect on cell viability to effect of negative controls Neg1 and Neg2, or single-sided molecules JQ1 and BI3812, or the additive effect of JQ1+BI3812. mean±s.d., 72 h drug treatment. b. TCIP1 EC50 of cell viability is anti-correlated with BCL6 content across 14 different cancer cell lines, p-values computed by Students’ t-test, two-sided, not adjusted for multiple comparisons. For a,b: n = 4 biological replicates with 3 technical replicates each, mean±s.d 72 h drug treatment. c. Measurement of BCL6, BRD4, p53 and BCL2 status of DLBCL cell lines ranked from left to right from high to low-BCL6 protein content. d. Unbiased screen of the effect of TCIP1 on the viability of 906 barcoded cancer cell lines (PRISM). Drug was dosed for 120 h in triplicate (Methods).
Extended Data Fig. 2 Rescue of TCIP1-induced cell death by competitive titration of BCL6 inhibitors.
a. Rescue of TCIP1-induced cell death across cancer cell lines that are highly sensitive to TCIP1, b. moderately sensitive, or c. not at all sensitive. d. Comparison of JQ1, TCIP1, and Neg2, which contains a functional BRD4 inhibitor but very low-affinity BCL6 binder (KD ~ 10 µM) in e. cell lines that have low or no BCL6. For a,b,c, e: n = 3 biological replicates, mean±s.d. Viability curves in a, b, c, and e are after 72 h drug treatment.
Extended Data Fig. 3 Biochemical studies of ternary complex binding affinities of TCIPs.
a. Ternary complex formation by TCIPs with related chemistries. TCIP1 plotted on every graph as a comparison. Each point represents an independent replicate which is the mean of 3 technical repeats, mean value line drawn. b. Isothermal calorimetry experiments to measure binary affinities of TCIP1 to BRD4BD1, BCL6BTB, and associated controls. Representative data from 1-2 independent experiments shown. c. Representative biolayer interferometry measurements (BLI) of ternary complex kinetics from 3 independent replicates shown with biotinylated BCL6BTB on the tip and excess BRD4BD1 in the well with titration of TCIP1. d. Off-rate and e. half-life of TCIP1 calculated from BLI dissociation curve measurements, 7-8 different doses for each of n = 3 independent replicates, mean±s.d. f. Area under curve of TR-FRET correlates with potency of TCIPs on cell death (KARPAS422 cells, viability at 72 h). Representative cellular EC50s labeled, mean of 4 biological replicates. Each area under the curve point represents an independent replicate which is the mean of 3 technical repeats of the TR-FRET experiment.
Extended Data Fig. 4 TCIP1 induces apoptosis at every stage of the cell cycle.
a. Dose-dependent induction of apoptosis at 24 h by TCIP1 as measured by AnnexinV-positive cells. b. Kinetics of TCIP1-induced apoptosis in KARPAS422 cells. For a, b: n = 2-6 biological replicates, mean(±s.d) shown as appropriate. c. Design of assay to measure cell cycle progression simultaneously with apoptosis using Terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) staining. d. TCIP1 induction of cell cycle arrest compared to controls, representative of 2 biological replicates, see Extended Data Fig. 5 for both replicates’ flow cytometry graphs. e. 100 nM TCIP1 induction of apoptosis as measured by DNA fragmentation at each stage of the cell cycle, n = 2 biological replicates, mean shown. f. Measurement of cell viability after cell cycle arrest in G0/G1 by serum starvation in SUDHL5 cells and TCIP1 addition, n = 3 biological replicates, mean±s.d.
Extended Data Fig. 5 Cell-cycle block and apoptosis induction by TCIP1.
a. 100 nM TCIP1 addition at 24 h and simultaneous measurement of cell cycle block and apoptosis in KARPAS422 cells, two separate experiments on different passages of cells shown. Gates were set based on no-stain controls detailed in the Supplementary information.
Extended Data Fig. 6 Robust and dose-dependent gene regulation by TCIP1.
a. Principal component analysis of RNA-seq data after addition of TCIP1 for 20 h in 2 biological replicates of KARPAS422 cells. b. Gene expression changes after addition of 100 nM TCIP1 for 20 h in KARPAS422 cells. Adjusted p-values computed by two-sided Wald test and adjusted for multiple comparisons by Benjamini-Hochberg. Significance cutoffs were padj ≤ 0.05 and |log2(Drug/DMSO)| ≥ 1), n = 2 biological replicates. c. Dose-dependent change in gene expression. d. Enrichment analysis of upregulated genes (MSigDB Hallmark Pathways). e. Analysis of TF binding at the top upregulated genes in over 4,500 public transcription factor ChIP-seq datasets from blood-lineage cells. For d, e: adjusted p-values computed by two-sided Fisher’s exact test and adjusted for multiple comparisons by Benjamini-Hochberg.
Extended Data Fig. 7 Specific activation of gene expression by TCIP1 but not related controls.
a. Gene expression changes after 1 h or 4 h addition of 10 nM TCIP1 in KARPAS422 cells. Changes at 2 h was shown in Fig. 4a. Adjusted p-values computed by two-sided Wald test and adjusted for multiple comparisons by Benjamini-Hochberg. Significance cutoffs were padj ≤ 0.05 and |log2(Drug/DMSO)| ≥ 0.5), n = 3 biological replicates. b. Specific effects of TCIP1 across transcriptome. For Neg1 and Neg2, n = 2 biological replicates. For TCIP1, n = 3 biological replicates. c. Enrichment analysis of upregulated and downregulated genes (MSigDB Hallmark Pathways). Adjusted p-values computed by two-sided Fisher’s exact test and adjusted for multiple comparisons by Benjamini-Hochberg.
Extended Data Fig. 8 ChIP-seq analyses of BRD4, H3K27ac, and RNA Pol II in response to TCIP1.
a. PCA plots of each ChIP-seq experiment at indicated timepoints of 10 nM TCIP1 addition: 0hr (DMSO), 15 min, 1 h, 2 h, and 4 h. b. Browser tracks of Pol II Ser2 phos, Pol II Ser5 phos, H3K27ac, and BRD4 at BCL6-target genes and TCIP1-upregulated genes FOXO3 and BCL2L11/BIM. c. Volcano plots of Pol II ser 2 phos, Pol II ser 5 phos, and H3K27ac after 2 h 10 nM TCIP1 addition. Adjusted p-values computed by two-sided Wald test and adjusted for multiple comparisons by Benjamini-Hochberg. Peaks were classified as differential after reads in peaks-based regulative log expression (RLE) normalization and cutoffs padj ≤ 0.05 and |log2(Drug/DMSO)|≥0.5. d. Enhancer and super-enhancer classification in KARPAS422 cells based on H3K27ac ChIP-seq and the ROSE algorithm (Methods). e. BRD4 and H3K27ac ChIP-seq track at the known OCA-B super-enhancer after TCIP1 addition for indicated timepoints. In b, e, Pol II Ser2 phos, Pol II Ser5 phos, and H3K27ac tracks in are spike-in- and input-normalized, BRD4 tracks are sequence-depth- and input-normalized.
Extended Data Fig. 9 Conversion of BCL6 auto-inhibitory pathway to feedforward loop.
a. Control Neg1 and Neg2 effect on BCL6 protein levels at 20 h treatment in KARPAS422 cells. b. Effect on BRD4 levels at 20 h treatment with TCIP1 in KARPAS422 cells. c. Kinetics of BCL6 upregulation in two separate DLBCL cell lines, KARPAS422 and SUDHL5, after addition of 10 nM TCIP1. Blots in a–c representative of 2 biological replicates (for SUDHL5) or 3 (for KARPAS422). d. Model for conversion of BCL6 auto-inhibitory circuit to a positive feedback loop.
Supplementary information
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This file contains Supplementary Notes regarding the chemical synthesis and protein constructs and purification, Supplementary Figs 1–2, and a full description for Supplementary Table 1 (table supplied separately)
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Mouse Tissue RNA-seq
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Gourisankar, S., Krokhotin, A., Ji, W. et al. Rewiring cancer drivers to activate apoptosis. Nature 620, 417–425 (2023). https://doi.org/10.1038/s41586-023-06348-2
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DOI: https://doi.org/10.1038/s41586-023-06348-2
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