Abstract
Adult liver malignancies, including intrahepatic cholangiocarcinoma and hepatocellular carcinoma, are the second leading cause of cancer-related deaths worldwide. Most individuals are treated with either combination chemotherapy or immunotherapy, respectively, without specific biomarkers for selection. Here using high-throughput screens, proteomics and in vitro resistance models, we identify the small molecule YC-1 as selectively active against a defined subset of cell lines derived from both liver cancer types. We demonstrate that selectivity is determined by expression of the liver-resident cytosolic sulfotransferase enzyme SULT1A1, which sulfonates YC-1. Sulfonation stimulates covalent binding of YC-1 to lysine residues in protein targets, enriching for RNA-binding factors. Computational analysis defined a wider group of structurally related SULT1A1-activated small molecules with distinct target profiles, which together constitute an untapped small-molecule class. These studies provide a foundation for preclinical development of these agents and point to the broader potential of exploiting SULT1A1 activity for selective targeting strategies.
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Data availability
The RNA-sequencing dataset assessing YC-1 treatment has been deposited to the Gene Expression Omnibus, available with accession number GSE168791. MS raw data can be accessed through the MassIVE data repository (massive.ucsd.edu) under accession numbers MSV000090805 and MSV000090808. The human pan-cancer data were derived from the TCGA Research Network at http://cancergenome.nih.gov/. The dataset derived from this resource that supports the findings of this study is available at https://ucsc-xena.gitbook.io/project/cite-us.
Source data are provided with this paper. All other data supporting the findings of this study are available from the corresponding authors on reasonable request.
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Acknowledgements
We thank Y. Jiang (Department of Chemistry and Chemical Biology, Harvard University) for helpful scientific discussions and experimental assistance. We thank D. Tao and E. Padilha (both from NCATS) for performing the metabolite experiments. This work was supported by the MGH Fund for Medical Discovery Award (to L.S.), the Cholangiocarcinoma Foundation Christopher J. Wilke Memorial Research Fellowship (to L.S.), NIH/NCI 1K99CA245194-01 and MGH Excellence Award (to L.S.), the V Foundation for Cancer Research (to N.B. and C.H.B.), the Department of Defense Translational Team Science Award W81XWH-17-1-0491 (to N.B.), NCI SPORE P50 CA127003 (to L.S., N.B. and C.H.B.) and the Gallagher Chair in Gastrointestinal Cancer Research, ProjectLiv and Target Cancer Foundation (to N.B.).
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Authors and Affiliations
Contributions
Conceptualization: L.S., M.I.D., K.K., W.Z., M.B.B., M.S., M.D.H., C.H.B. and N.B. Methodology: L.S., W.S., P.V., M.I.D., J.K., P.G., J.E., J.S., S.P., M.B.B., M.S., M.Z., H.K., W.Y.K., B.Z.S., D.T.T., C.R.F., M.D.H., C.H.B., W.H. and N.B. Software: P.G., M.Z., W.H. and C.H.B. Formal analysis: L.S., P.G., J.S., M.Z., U.M., M.S., V.D., W.H. and C.H.B. Investigation: L.S., W.S., P.V., S.K.S., R.A., J.K., R.E., T.D.L., J.H.S., W.Z., T.-Y.W., J.E., J.S., U.M., V.W., H.K., V.V., M.-J.W., S.E.K., J.H., J.M., E.M. and G.S.C. Resources: L.S., M.I.D., K.K., M.B.B., M.S., B.Z.S., D.T.T., C.R.F., M.D.H., C.H.B., W.H. and N.B. Data curation: L.S., M.S., C.H.B., P.G., M.I.D., M.D.H., C.H.B. and W.H. Writing, original draft: L.S., W.S., K.K., M.D.H. and N.B. Writing, review and editing: L.S., W.S., K.K., M.S., W.Y.K., B.Z.S., C.H.B., M.D.H. and N.B. Visualization: L.S., W.S. and K.K. Supervision: W.Y.K., B.Z.S., C.R.F., C.H.B., M.D.H. and N.B. Project administration: S.P., M.D.H. and N.B. Funding acquisition: L.S., M.D.H. and N.B.
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Competing interests
Patent applications for the drug uses detailed in this manuscript have been filed by L.S. and N.B. N.B. reports research funding from Kinnate Biopharma, Taiho Oncology, Relay, Bristol Myers Squibb and Servier Laboratories. B.Z.S. has received consulting fees from Boehringer-Ingelheim and iTeos Therapeutics. D.T.T. is a founder of and owns equity in TellBio, Inc., ROME Therapeutics and PanTher Therapeutics and has received consulting fees from Merrimack Pharmaceuticals, Ventana Roche, Foundation Medicine, Inc., and EMD Millipore Sigma. The other authors declare no other competing interests.
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Extended data
Extended Data Fig. 1 Characterization of cellular response to YC-1.
a, RBE cells were treated with YC-1 (1 μM) or vehicle for the indicated times and then stained with crystal violet. Data are quantified in the lower graph. Data shown were from one of the three performed experiments with similar results. b, RBE cells synchronized at the entry of S phase by double thymidine block were treated with YC-1 (1 μM) or vehicle and at the same time released into S phase. The DNA content (PI) and DNA synthesis (EdU incorporation) were analyzed after 4 hours by flow cytometry. Refer to Supplementary Fig. 1 for gating strategy. c, Cleaved caspase-3 assay showing that YC-1 selectively induces apoptosis in responsive ICC cell lines (RBE and SNU1079 are IDH1 mutant). Data shown were from one of the two performed experiments with similar results. d–g, Analysis of RBE cells treated with YC-1 or DMSO. (d) Quantitative proteomics for cyclin and CDK protein levels, n = 2 biologically independent cell lines (RBE and SNU1079); Immunoblots for (e) cell cycle markers and (f) stress response markers. Immunoblots in (e) and (f) were performed two times with similar results. (g) Heatmap of YC-1-induced gene expression changes in p53 and apoptosis pathways. Error bars (a, c, d) are mean ± s.d.
Extended Data Fig. 2 YC-1 sensitivity does not correlate to known mechanisms of action.
a, A panel of cancer cell lines, including IDHm and IDH WT ICC cells lines used in the initial chemical screens (shaded columns), were profiled for sensitivity to known HIF1α inhibitors and sGC agonists with highest dose of ~40 μM covering the effective range. Square size denotes sensitivity (AUC) of each cell line to a given compound, square color denotes the efficacy of the compound. b, Scatter plot of normalized CRISPR dependency scores (DepMap) of HIF1A, HIF2A (EPAS1) and VHL (as control) in hepatobiliary cell lines.
Extended Data Fig. 3 YC-1 sensitivity is determined by SULT1A1 expression levels.
a, Correlation between SULT1A1 mRNA and protein across biliary tract cancer cell lines. Significance was analyzed using two-tailed Student’s t-test. P < 0.05 was considered statistically significant. b, RBE control cells (parental and sgGFP cells) or CRISPR-induced SULT1A1 KO derivatives (CSK1-6) were tested for sensitivity to YC-1 (left) or dasatinib (right). SSP25 is an insensitive cell line shown as reference. Immunoblot confirming loss of SULT1A1 is shown in (c). Data show two biological replicates with similar results. d, ICC20 control sgGFP engineered cells or SULT1A1 KO derivatives (CSK1-3) were tested for sensitivity to YC-1 (left) or dasatinib (right). Immunoblot confirming loss of SULT1A1 is shown in (e). Data show two biological replicates with similar results. f, Schematic of CRISPR-resistant SULT1A1 expression constructs. g, Scatter plot showing ectopic expression of SULT1A1 sensitizes twelve hepatobiliary cancer cell lines to YC-1. The murine hepatocyte cell line, AML12, is not sensitized. Asterisk denotes IC50 value too high to extrapolate. h, Immunoblot of CCLP1 and SSP25 cells engineered to overexpressed naturally occurring SULT1A1 variants. i, YC-1 sensitivity of CCLP1 cells expressing SULT1A1 or SULT1A3. SULT1A1-1 (SULT1A1 V220M, V223M, F247L); SULT1A1-2 (SULT1A1 S44N, V164A, V223M); SULT1A1-3 (SULT1A1 V223M). Immunoblots (c, e, h) were performed two times with similar results. Two biologically independent replicates are shown (b, d, i right). Error bars in i, left panel are mean ± s.d. n = 4 biologically independent experiments.
Extended Data Fig. 4 SULT1A1 expression associates with hepatocyte lineage.
a, Full heatmap of hepatocyte protein expression in ICC cell lines according to GSEA using the phenotype of SULT1A1 protein levels. Serves as supporting data for Fig. 4b. b, SULT1A1 protein expression in biliary cancer PDXs. Corresponding genomic markers are annotated for hot-spot IDH1/IDH2 mutations, FGFR2 fusions, and mutation of BAP1.
Extended Data Fig. 5 Benzyl alcohol moiety determines YC-1 toxicity and defines a class of SULT1A1-activatable compounds.
a, Schematic of SULT1A1-mediated sulfonation reaction in modulating xenobiotic solubility. b, Response of RBE cells (IC50) to parent YC-1 or dehydroxylated analog. Two biologically independent experiments are shown. c, Computational modeling showing 2-dimentional depiction of YC-1 molecular interactions with amino acid residues within SULT1A1 catalytic site. Serves as supporting data for Fig. 4c. d, Exemplar compounds of each chemical group identified from the NCI-60 database as having activity profiles similar to YC-1. e, Scatter plot showing correlation between YC-1 and RITA sensitivity profiles across 398 cancer cell lines. Relative SULT1A1 mRNA levels are depicted by the color scheme. f, Graph showing the ranked activity of AHBA series compounds in terms of differential sensitivity toward SULT1A1-high cells (RBE and SNU1079) versus SULT1A1-low cells (SSP25 and CCLP1) (y axis). The color code represents the average sensitivity (AUC) of SULT1A1-high cells to each analog. Bubble sizes denote significance (P value). Significance was analyzed using two-tailed Student’s t-test. P < 0.05 was considered statistically significant.
Extended Data Fig. 6 Proteomic identification of YC-1 binding targets.
a, Dot blot of protein lysates from RBE parental cells (WT) RBE cells overexpressing SULT1A1 were treated with YC-1 biotin or DH-YC-1 biotin. Blots were probed with HRP-conjugated streptavidin (left). Ponceau S staining serves as the total protein loading control (right). b, Proteins extracted from RBE cells treated with YC-1 Biotin or DH-YC-1 Biotin were subjected to streptavidin affinity purification, digested to single amino acids, and analyzed by mass-spectrometry. Top: Heat map representing YC-1-conjugated amino acids. Bottom: Heat map representing amino acids not conjugated to YC-1 and serving to illustrate relative amino acid content of the purified proteins. c, Schematic of the predicted electrophilic reaction between sulfonated YC-1 biotin and lysine residue in proteins. d, Bar chart of odds ratios of enriched Gene Ontology classes among YC-1 binding proteins (bars to the left) in comparison to those from the most expressed 500 genes in RBE cells (bars to the right). e, Scatter plot of specific YC-1 binding score (x axis) and probability of gene dependency from the Broad DepMap (y axis) of YC-1 binding proteins. In (e), color code indicates proteins with common RNA binding domains identified by EnrichR analysis.
Extended Data Fig. 7 YC-1 covalently binds RNA processing factors and influences RNA splicing.
a, Immunoblot from streptavidin affinity purification validating YC-1 binding proteins. RBE cells were treated with YC-1 biotin or DH-YC-1 biotin control in the presence of excess non-biotinylated YC-1 or DH-YC-1 as indicated. Left: Expression of candidate YC-1 binding proteins in whole cell lysates. Right: Immunoblot after Streptavidin capture, showing dose-dependent competition by parent YC-1. b, Immunoblot from TARDBP immunoprecipitation validating direct YC-1 binding. RBE cells were treated as in (a). The immunoblots (a, b) were performed two times with similar results. c, Scatter plot of genes with altered intron retention identified from RNA-seq analysis of RBE and SNU1079 cells treated with YC-1 or vehicle for 6 and 16 hours. n = 3 biological replicates per condition. ΔRI is the intron retention score calculated by the SALMON software package. d, Left, a TARDBP splicing efficiency assay assessing SULT1A1 dependent YC-1 impact on TARDBP RNA splicing activity. 293 T cells exogenously expressing SULT1A1 or empty vector were transiently transfected with the reporter module containing plasmid and treated with YC-1 or DMSO vehicle and analyzed by fluorescent confocal microscope with GFP (G) and mCherry (R) laser. Statistical significance annotated between individual conditions (Welch unpaired t-test). n = 3 biologically independent experiments with cells from two independent images per experiment included (>500 cells in total). ‘n.s.’ denotes not significant. Right, YC-1 sensitivity assay confirming stable SULT1A1 expression. Two biologically independent experiments are shown. e, siRNA targeting TARDBP (left) or DDX42 (right) reduced target protein expression and cell number monitored for 5–6 d post transfection. Error bars in left panel are mean ± s.d. Data shown were from one of the two performed experiments with similar results.
Extended Data Fig. 8 SULT1A1 determines YC-1 efficacy in vivo.
a, SULT1A1-positive and SULT1A1-negative (control) CCLP1 cells were implanted subcutaneously into NSG mice. Once tumors reached ~100 mm3, mice were treated with YC-1 (50 mg/kg) or vehicle for 14 d. Mice were then monitored for disease progression in the absence of treatment. Left: Graph of individual serial tumor volumes. These data are presented in the form of mean volumes in Fig. 6b of the main figures. Right: Serial changes in body weight. Error bars are mean ± s.e.m. n = 5–6 independent animals per group. b–e, Study of SULT1A1-high expressing ICC21 xenografts in response to YC-1 treatment. b, YC-1 concentration was assayed with three independent ICC21 xenograft tumor samples with YC-1 or vehicle treatment by mass spectrometry. Dashed line marks the in vitro ICC21 sensitivity to YC-1 treatment (IC50). Error bars are mean ± s.d. n = 3 independent samples per group. c, Tissue sections of ICC21 orthotopic tumors (middle panels) and adjacent normal (left panels) subjected to H&E and TUNEL staining. TUNEL staining was quantified in graph at the right and two independent animals per group are shown. Scale bar, 100 µm. d, Serial changes in body weight (left) were monitored for three weeks for subcutaneous tumor-bearing mice on YC-1 treatment and the liver and body weight ratios (right) were recorded at the euthanization point. Error bars are mean ± s.e.m. n = 5 independent animals per group, two-tailed, unpaired Student’s t-test. e, table displaying plasma markers indicative of liver function from vehicle and YC-1 treated mouse plasma samples (P values derived by two-tailed, unpaired Student’s t-test).
Extended Data Fig. 9 SULT1A1 determines RITA efficacy in vivo.
a–d, Study of SULT1A1-dependent sensitivity of CORL105 xenograft model to RITA. CORL105 is an IDH1-R132C mutant lung adenocarcinoma cell line with high endogenous SULT1A1 levels, which has robust growth in vivo. a, Generation of CORL105 derivatives with CRISPR-mediated SULT1A1 KO. Upper, Immunoblot showing loss of SULT1A1 protein expression upon CRISPR-mediated deletion of SULT1A1 (CSK1-3) and robust SULT1A1 detection in parental CORL105 cells and control sgGFP cells. The immunoblot was performed two times with similar results. Lower, demonstration that CORL105 cells are highly sensitive to RITA in a SULT1A1-dependent manner. Two biologically independent experiments are shown. b, Representative immunohistochemistry staining from CORL105 control (sgGFP) and SULT1A1 KO (CSK2) xenografts, showing loss of staining with the SULT1A1 antibody in the SULT1A1 KO model. Serves as validation of SULT1A1 antibody specificity for IHC studies. Similar results were obtained in samples from 2–4 independent animals per group and three groups with independent sgRNA designs targeting SULT1A1 gene. Scale bar, 200 µm. c, Immunoblot confirming SULT1A1 protein loss in xenograft tumors generated from SULT1A1 KO CORL105 cells. The immunoblot was performed a single time, with multiple independent tumors analyzed per condition. d, Mice harboring tumors of 100–150 mm3 were treated with RITA (100 mg/kg) or vehicle daily with intermitted dosing breaks. Graphs show serial monitoring of group tumor volume (left), individual tumor volume (middle) and body weight (right). Dosing breaks are denoted by grey shading. Error bars are mean ± s.e.m. n = 5–10 independent animals per group.
Extended Data Fig. 10 SULT1A1 expression is prominent in liver cancers.
a, Normalized SULT1A1 RNA expression across bulk normal tissues (left, top 10) and single cell types (right, top 10) in human body (retrieved from proteinatlas.org). b, Box and whisker plot derived from TCGA analysis of SULT1A1 expression in patient samples showing top 32 tumor types ranked by median SULT1A1 mRNA expression. Liver cancer types (x axis) are coded red. Note that extrahepatic cholangiocarcinoma is negative for SULT1A1 (coded blue). The center of the box indicates the median, upper and lower lines indicate upper and lower quartiles and the mark with the greatest and lowest values indicate maximum and minimum. c, Representative immunohistochemical images of SULT1A1 staining in normal human liver from multiple patients, demonstrating expression in the hepatocytes. Arrows point to normal bile ducts with no SULT1A1 staining. Similar results were obtained from multiple samples from independent patients that were processed at independent times. Scale bar, 200 µm.
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Supplementary Table 13 and Fig. 1.
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Shi, L., Shen, W., Davis, M.I. et al. SULT1A1-dependent sulfonation of alkylators is a lineage-dependent vulnerability of liver cancers. Nat Cancer 4, 365–381 (2023). https://doi.org/10.1038/s43018-023-00523-0
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DOI: https://doi.org/10.1038/s43018-023-00523-0
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