Abstract
Gemcitabine is a potent inhibitor of DNA replication and is a mainstay therapeutic for diverse cancers, particularly pancreatic ductal adenocarcinoma (PDAC). However, most tumors remain refractory to gemcitabine therapies. Here, to define the cancer cell response to gemcitabine, we performed genome-scale CRISPR–Cas9 chemical–genetic screens in PDAC cells and found selective loss of cell fitness upon disruption of the cytidine deaminases APOBEC3C and APOBEC3D. Following gemcitabine treatment, APOBEC3C and APOBEC3D promote DNA replication stress resistance and cell survival by deaminating cytidines in the nuclear genome to ensure DNA replication fork restart and repair in PDAC cells. We provide evidence that the chemical–genetic interaction between APOBEC3C or APOBEC3D and gemcitabine is absent in nontransformed cells but is recapitulated across different PDAC cell lines, in PDAC organoids and in PDAC xenografts. Thus, we uncover roles for APOBEC3C and APOBEC3D in DNA replication stress resistance and offer plausible targets for improving gemcitabine-based therapies for PDAC.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 digital issues and online access to articles
$119.00 per year
only $9.92 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
Data availability
FASTQ files from Illumina sequencing of the CRISPR–Cas9 screens have been deposited in the National Center for Biotechnology Information Sequence Read Archive (https://www.ncbi.nlm.nih.gov/sra; study accession PRJNA844572). RNA sequencing data for the ICGC PACA samples is available from the ICGC data portal (https://dcc.icgc.org/releases/release_28/Projects/PACA-CA) and at https://ega-archive.org/datasets/EGAD00001006081 for samples from the COMPASS clinical trial cohort. RNA expression data for PDAC cell lines and organoids are available from the Broad Institute’s DepMap portal (DepMap Cancer Cell Line Encyclopedia (CCLE) Public 23Q2; https://depmap.org/portal/download/) and the TCGA data portal (https://portal.gdc.cancer.gov/projects/TCGA-PAAD) for PDAC tumors. Source data for Figs. 1–7 and Extended Data Figs. 1–9 are available with this article. Further information on experimental design and software is available in the Nature Research Reporting Summary linked to this article. All other data supporting the findings of this study are available from the corresponding author upon reasonable request. Source data are provided with this paper.
Code availability
No nonstandard code was used to generate, analyze or plot any of the data presented in this study.
References
Gorgoulis, V. G. et al. Activation of the DNA damage checkpoint and genomic instability in human precancerous lesions. Nature 434, 907–913 (2005).
Bartkova, J. et al. DNA damage response as a candidate anti-cancer barrier in early human tumorigenesis. Nature 434, 864–870 (2005).
Knijnenburg, T. A. et al. Genomic and molecular landscape of DNA damage repair deficiency across The Cancer Genome Atlas. Cell Rep. 23, 239–254 (2018).
Ubhi, T. & Brown, G. W. Exploiting DNA replication stress for cancer treatment. Cancer Res. 79, 1730–1739 (2019).
Hertel, L. W. et al. Evaluation of the antitumor activity of gemcitabine (2′,2′-difluoro-2′-deoxycytidine). Cancer Res. 50, 4417–4422 (1990).
Heinemann, V. et al. Inhibition of ribonucleotide reduction in CCRF–CEM cells by 2′,2′-difluorodeoxycytidine. Mol. Pharmacol. 38, 567–572 (1990).
Huang, P., Chubb, S., Hertel, L. W., Grindey, G. B. & Plunkett, W. Action of 2′,2′-difluorodeoxycytidine on DNA synthesis. Cancer Res. 51, 6110–6117 (1991).
Jones, R. M., Kotsantis, P., Stewart, G. S., Groth, P. & Petermann, E. BRCA2 and RAD51 promote double-strand break formation and cell death in response to gemcitabine. Mol. Cancer Ther. 13, 2412–2421 (2014).
George, J. W., Bessho, M. & Bessho, T. Inactivation of XPF sensitizes cancer cells to gemcitabine. J. Nucleic Acids 2019, 6357609 (2019).
Smith, S. C. et al. A gemcitabine sensitivity screen identifies a role for NEK9 in the replication stress response. Nucleic Acids Res. 42, 11517–11527 (2014).
Tsai, M. S., Kuo, Y. H., Chiu, Y. F., Su, Y. C. & Lin, Y. W. Down-regulation of Rad51 expression overcomes drug resistance to gemcitabine in human non–small-cell lung cancer cells. J. Pharmacol. Exp. Ther. 335, 830–840 (2010).
Parvathaneni, S. & Sharma, S. The DNA repair helicase RECQ1 has a checkpoint-dependent role in mediating DNA damage responses induced by gemcitabine. J. Biol. Chem. 294, 15330–15345 (2019).
Von Hoff, D. D. et al. Increased survival in pancreatic cancer with nab-paclitaxel plus gemcitabine. N. Engl. J. Med. 369, 1691–1703 (2013).
Klanova, M. et al. Downregulation of deoxycytidine kinase in cytarabine-resistant mantle cell lymphoma cells confers cross-resistance to nucleoside analogs gemcitabine, fludarabine and cladribine, but not to other classes of anti-lymphoma agents. Mol. Cancer 13, 159 (2014).
Karnitz, L. M. et al. Gemcitabine-induced activation of checkpoint signaling pathways that affect tumor cell survival. Mol. Pharmacol. 68, 1636–1644 (2005).
Hopkins, J. F. et al. Mutations in mitochondrial DNA from pancreatic ductal adenocarcinomas associate with survival times of patients and accumulate as tumors progress. Gastroenterology 154, 1620–1624.e5 (2018).
Hudson, T. J. et al. International network of cancer genome projects. Nature 464, 993–998 (2010).
Dungrawala, H. et al. The replication checkpoint prevents two types of fork collapse without regulating replisome stability. Mol. Cell 59, 998–1010 (2015).
Rodríguez, M. G., Flath, B. & Chelico, L. The interesting relationship between APOBEC3 deoxycytidine deaminases and cancer: a long road ahead. Open Biol. 10, 200188 (2020).
Buisson, R., Lawrence, M. S., Benes, C. H. & Zou, L. APOBEC3A and APOBEC3B activities render cancer cells susceptible to ATR inhibition. Cancer Res. 77, 4567–4578 (2017).
Venkatesan, S. et al. Induction of APOBEC3 exacerbates DNA replication stress and chromosomal instability in early breast and lung cancer evolution. Cancer Discov. 11, 2456–2473 (2021).
Wörmann, S. M. et al. APOBEC3A drives deaminase domain-independent chromosomal instability to promote pancreatic cancer metastasis. Nat. Cancer 2, 1338–1356 (2021).
Langenbucher, A. et al. An extended APOBEC3A mutation signature in cancer. Nat. Commun. 12, 1602 (2021).
Petljak, M. et al. Mechanisms of APOBEC3 mutagenesis in human cancer cells. Nature 607, 799–807 (2022).
Caswell, D. R. et al. The role of APOBEC3B in lung tumor evolution and targeted cancer therapy resistance. Nat. Genet. 56, 60–73 (2024).
Law, E. K. et al. The DNA cytosine deaminase APOBEC3B promotes tamoxifen resistance in ER-positive breast cancer. Sci. Adv. 2, e1601737 (2016).
Isozaki, H. et al. Therapy-induced APOBEC3A drives evolution of persistent cancer cells. Nature 620, 393–401 (2023).
Nowarski, R. et al. APOBEC3G enhances lymphoma cell radioresistance by promoting cytidine deaminase-dependent DNA repair. Blood 120, 366–375 (2012).
Britan-Rosich, Y. et al. APOBEC3G protects the genome of human cultured cells and mice from radiation-induced damage. FEBS J. 290, 1822–1839 (2023).
Qian, Y. et al. Aberrant APOBEC3C expression induces characteristic genomic instability in pancreatic ductal adenocarcinoma. Oncogenesis 11, 35 (2022).
Peng, G., Ke, J. L., Jin, W., Greenwell-Wild, T. & Wahl, S. M. Induction of APOBEC3 family proteins, a defensive maneuver underlying interferon-induced anti–HIV-1 activity. J. Exp. Med. 203, 41–46 (2006).
Argyris, E. G. et al. The IFN-induced expression of APOBEC3G in human blood brain barrier exerts a potent intrinsic immunity to block HIV-1 entry to central nervous system. Virology 367, 440 (2007).
Stenglein, M. D., Burns, M. B., Li, M., Lengyel, J. & Harris, R. S. APOBEC3 proteins mediate the clearance of foreign DNA from human cells. Nat. Struct. Mol. Biol. 17, 222–229 (2010).
Wang, Z. et al. APOBEC3 deaminases induce hypermutation in human papillomavirus 16 DNA upon beta interferon stimulation. J. Virol. 88, 1308–1317 (2014).
Niocel, M., Appourchaux, R., Nguyen, X. N., Delpeuch, M. & Cimarelli, A. The DNA damage induced by the cytosine deaminase APOBEC3A leads to the production of ROS. Sci. Rep. 9, 4714 (2019).
Conner, K. L. et al. APOBEC3 enzymes mediate efficacy of cisplatin and are epistatic with base excision repair and mismatch repair in platinum response. NAR Cancer 2, zcaa033 (2021).
Oh, S. et al. Genotoxic stress and viral infection induce transient expression of APOBEC3A and pro-inflammatory genes through two distinct pathways. Nat. Commun. 12, 4917 (2021).
Kanu, N. et al. DNA replication stress mediates APOBEC3 family mutagenesis in breast cancer. Genome Biol. 17, 185 (2016).
Tao, L., Jiang, Z., Xu, M., Xu, T. & Liu, Y. Induction of APOBEC3C facilitates the genotoxic stress-mediated cytotoxicity of artesunate. Chem. Res. Toxicol. 32, 2526–2537 (2019).
Lin, L. et al. Comprehensive mapping of key regulatory networks that drive oncogene expression. Cell Rep. 33, 108426 (2020).
Botvinnik, A. et al. APOBEC3G rescues cells from the deleterious effects of DNA damage. FEBS J. 288, 6063–6077 (2021).
Constantin, D., Dubuis, G., Conde-Rubio, M. D. C. & Widmann, C. APOBEC3C, a nucleolar protein induced by genotoxins, is excluded from DNA damage sites. FEBS J. 289, 808–831 (2022).
Wijesinghe, P. & Bhagwat, A. S. Efficient deamination of 5-methylcytosines in DNA by human APOBEC3A, but not by AID or APOBEC3G. Nucleic Acids Res. 40, 9206–9217 (2012).
Schutsky, E. K., Nabel, C. S., Davis, A. K. F., DeNizio, J. E. & Kohli, R. M. APOBEC3A efficiently deaminates methylated, but not TET-oxidized, cytosine bases in DNA. Nucleic Acids Res. 45, 7655–7665 (2017).
Buisson, R. et al. Passenger hotspot mutations in cancer driven by APOBEC3A and mesoscale genomic features. Science 364, eaaw2872 (2019).
Neff, T. & Blau, C. A. Forced expression of cytidine deaminase confers resistance to cytosine arabinoside and gemcitabine. Exp. Hematol. 24, 1340–1346 (1996).
Frese, K. K. et al. Nab-paclitaxel potentiates gemcitabine activity by reducing cytidine deaminase levels in a mouse model of pancreatic cancer. Cancer Discov. 2, 260–269 (2012).
Pecori, R., Di Giorgio, S., Paulo Lorenzo, J. & Nina Papavasiliou, F. Functions and consequences of AID/APOBEC-mediated DNA and RNA deamination. Nat. Rev. Genet. 23, 505–518 (2022).
Róna, G. et al. Detection of uracil within DNA using a sensitive labeling method for in vitro and cellular applications. Nucleic Acids Res. 44, e28 (2016).
Mohni, K. N. et al. HMCES maintains genome integrity by shielding abasic sites in single-strand DNA. Cell 176, 144–153.e13 (2019).
Liu, W. et al. RAD51 bypasses the CMG helicase to promote replication fork reversal. Science 380, 382–387 (2023).
Thompson, P. S. & Cortez, D. New insights into abasic site repair and tolerance. DNA Repair 90, 102866 (2020).
Chen, Y. W., Cleaver, J. E., Hanaoka, F., Chang, C. F. & Chou, K. M. A novel role of DNA polymerase eta in modulating cellular sensitivity to chemotherapeutic agents. Mol. Cancer Res. 4, 257–265 (2006).
Huttlin, E. L. et al. Dual proteome-scale networks reveal cell-specific remodeling of the human interactome. Cell 184, 3022–3040.e28 (2021).
Hultquist, J. F. et al. Human and Rhesus APOBEC3D, APOBEC3F, APOBEC3G, and APOBEC3H demonstrate a conserved capacity to restrict Vif-deficient HIV-1. J. Virol. 85, 11220–11234 (2011).
Lackey, L., Law, E. K., Brown, W. L. & Harris, R. S. Subcellular localization of the APOBEC3 proteins during mitosis and implications for genomic DNA deamination. Cell Cycle 12, 762–772 (2013).
Takei, H. et al. Alternative splicing of APOBEC3D generates functional diversity and its role as a DNA mutator. Int. J. Hematol. 112, 395–408 (2020).
Oughtred, R. et al. The BioGRID database: a comprehensive biomedical resource of curated protein, genetic, and chemical interactions. Protein Sci. 30, 187–200 (2021).
Mehta, K. P. M., Lovejoy, C. A., Zhao, R., Heintzman, D. R. & Cortez, D. HMCES maintains replication fork progression and prevents double-strand breaks in response to APOBEC deamination and abasic site formation. Cell Rep. 31, 107705 (2020).
Talluri, S. et al. Dysregulated APOBEC3G causes DNA damage and promotes genomic instability in multiple myeloma. Blood Cancer J. 11, 1–11 (2021).
Tiriac, H. et al. Organoid profiling identifies common responders to chemotherapy in pancreatic cancer. Cancer Discov. 8, 1112–1129 (2018).
Noordermeer, S. M. et al. The shieldin complex mediates 53BP1-dependent DNA repair. Nature 560, 117–121 (2018).
Olivieri, M. et al. A genetic map of the response to DNA damage in human cells. Cell 182, 481–496 (2020).
Kim, E. & Hart, T. Improved analysis of CRISPR fitness screens and reduced off-target effects with the BAGEL2 gene essentiality classifier. Genome Med. 13, 1–11 (2021).
Mi, H., Muruganujan, A., Casagrande, J. T. & Thomas, P. D. Large-scale gene function analysis with the PANTHER classification system. Nat. Protoc. 8, 1551–1566 (2013).
Supek, F., Bošnjak, M., Škunca, N. & Šmuc, T. REVIGO summarizes and visualizes long lists of gene ontology terms. PLoS ONE 6, e21800 (2011).
Bookout, A. L., Cummins, C. L., Mangelsdorf, D. J., Pesola, J. M. & Kramer, M. F. High-throughput real-time quantitative reverse transcription PCR. Curr. Protoc. Mol. Biol. 73, 15.8.1–15.8.28 (2006).
Connor, A. A. et al. Integration of genomic and transcriptional features in pancreatic cancer reveals increased cell cycle progression in metastases. Cancer Cell 35, 267–282.e7 (2019).
Chan-Seng-Yue, M. et al. Transcription phenotypes of pancreatic cancer are driven by genomic events during tumor evolution. Nat. Genet. 52, 231–240 (2020).
O’Kane, G. M. et al. GATA6 expression distinguishes classical and basal-like subtypes in advanced pancreatic cancer. Clin. Cancer Res. 26, 4901–4910 (2020).
Jaguva Vasudevan, A. A. et al. Enhancing the catalytic deamination activity of APOBEC3C is insufficient to inhibit Vif-deficient HIV-1. J. Mol. Biol. 429, 1171–1191 (2017).
Crain, P. F. Preparation and enzymatic hydrolysis of DNA and RNA for mass spectrometry. Methods Enzymol. 193, 782–790 (1990).
Bapiro, T. E. et al. A novel method for quantification of gemcitabine and its metabolites 2′,2′-difluorodeoxyuridine and gemcitabine triphosphate in tumour tissue by LC–MS/MS: comparison with 19F NMR spectroscopy. Cancer Chemother. Pharmacol. 68, 1243–1253 (2011).
Pálinkás, H. L. et al. Genome-wide alterations of uracil distribution patterns in human DNA upon chemotherapeutic treatments. eLife 9, e60498 (2020).
Horváth, A. & Vértessy, B. G. A one-step method for quantitative determination of uracil in DNA by real-time PCR. Nucleic Acids Res. 38, e196 (2010).
O’Brien, S. et al. FBXW7-loss sensitizes cells to ATR inhibition through induced mitotic catastrophe. Cancer Res. Commun. 3, 2596–2607 (2023).
Gallo, D., Wang, G., Yip, C. M. & Brown, G. W. Analysis of replicating yeast chromosomes by DNA combing. Cold Spring Harb. Protoc. 2016, pdp.prot085118 (2016).
Yvon, A.-M. C., Wadsworth, P. & Jordan, M. A. Taxol suppresses dynamics of individual microtubules in living human tumor cells. Mol. Biol. Cell 10, 947–959 (1999).
Weiss, J. et al. Optimizing drug response study design in patient-derived tumor xenografts. Cancer Inform. 21, 11769351221136056 (2022).
Gallo, D. et al. CCNE1 amplification is synthetic lethal with PKMYT1 kinase inhibition. Nature 604, 749–756 (2022).
Acknowledgements
We thank S. Angers (University of Toronto) for the Cas9 stable HPAF-II, AsPC-1 and BxPC-3 cell lines, D. Durocher (Lunenfeld-Tanenbaum Research Institute) for the RPE1–hTERT–Cas9 p53−/− cell line and the lentiGuide–NLS–eGFP and lentiGuide–NLS–mCherry plasmids, H. Wyatt (University of Toronto) for the Sf9 and Hi5 insect cell lines, L. Chelico (University of Saskatchewan) for A3C purification advice and the Donnelly Sequencing Centre (University of Toronto) for performing Illumina sequencing. Pancreatic tumor RNA expression data from the ICGC PACA-CA and COMPASS trial cohorts were generated by the Ontario Institute for Cancer Research (PanCuRx Translational Research Initiative) through funding provided by the Government of Ontario, the Wallace McCain Centre for Pancreatic Cancer supported by the Princess Margaret Cancer Foundation, the Terry Fox Research Institute, the Canadian Cancer Society Research Institute, Pancreatic Cancer Canada and a charitable donation from the Canadian Friends of the Hebrew University (A.U. Soyka). T.U. was supported by an Ontario Graduate Scholarship and a Frederick Banting and Charles Best Canadian Institutes of Health Research Doctoral Award. J.M. was supported by the Canadian Institutes of Health Research (PJT-463531) and holds the GlaxoSmithKline Chair in Genetics and Genome Biology at the Hospital for Sick Children. Project no. FK137867 for A.B. has been implemented with support provided by the Ministry for Culture and Innovation of Hungary from the National Research, Development and Innovation Fund, financed under the OTKA_FK_21 funding scheme. A.B. was also supported by the János Bolyai Research Scholarship of the Hungarian Academy of Science (BO/726/22/8) and by the ÚNKP-22-5-BME-295 New National Excellence Program of the Ministry for Culture and Innovation from the source of the National Research, Development and Innovation Fund. B.G.V. was supported by the National Research, Development and Innovation Fund of Hungary (K135231, NKP-2018-1.2.1-NKP-2018-00005) and the TKP2021-EGA-02 grant, implemented with support provided by the Ministry for Innovation and Technology of Hungary from the National Research, Development and Innovation Fund. D.A.T. is supported by the Lustgarten Foundation, where D.A.T. is a distinguished scholar and Director of the Lustgarten Foundation–designated Laboratory of Pancreatic Cancer Research. D.A.T. is also supported by the Thompson Foundation, the Cold Spring Harbor Laboratory and Northwell Health Affiliation, the Northwell Health Tissue Donation Program, the Cold Spring Harbor Laboratory Association, the National Institutes of Health (5P30CA45508, U01CA210240, R01CA229699, U01CA224013, 1R01CA188134 and 1R01CA190092) and by a gift from the Simons Foundation (552716). H.L.R. was funded by the Canadian Institutes of Health Research (FRN 166094) and Canada Foundation for Innovation (grant 37660). G.W.B. is a Tier I Canada Research Chair and was supported by the Canadian Cancer Society (Innovate grant 706293), the Canadian Institutes of Health Research (FDN-159913) and the Ontario Institute for Cancer Research (P.CTIP.1005). We are grateful to work on the lands of the Mississaugas of the Credit, the Anishnaabeg, the Haudenosaunee and the Wendat peoples, land that is now home to many diverse First Nations, Inuit and Métis peoples.
Author information
Authors and Affiliations
Contributions
T.U. designed the experiments, performed most of the experimental work, analyzed all experimental data, wrote and revised the manuscript with input from all authors, and prepared all data figures. O.Z. and A.T.Q. performed LC–MS/MS analysis of genomic and intracellular cytidine and gemcitabine metabolite levels. D.P. provided technical assistance on organoids and helped with generating stable PDAC organoid cultures for the drug sensitivity experiments. P.C. and N.-A.P. performed the mouse xenograft experiments. A.B. performed the dot blot analyses to quantify genomic uracil levels. G.-H.J., G.M.O. and F.N. prepared, processed and analyzed PDAC tumor tissues for the RNA sequencing datasets used for APOBEC3 expression analyses. J.M. provided technical advice and reagents for the CRISPR–Cas9 screens with gemcitabine. G.M.O., F.N., J.M.W. and S.G. supervised G.-H.J. B.G.V. supervised A.B. D.A.T. supervised D.P. H.L.R. supervised O.Z. and A.T.Q. G.W.B. supervised the study, wrote and revised the manuscript with input from all authors, and acquired funding.
Corresponding author
Ethics declarations
Competing interests
D.A.T. receives stock options from Leap Therapeutics, Surface Oncology, Sonata Therapeutics and Mestag Therapeutics outside of the published work. D.A.T. is scientific cofounder of Mestag Therapeutics. D.A.T. has received research grant support from the Fibrogen, Mestag and ONO Therapeutics. D.A.T. receives grant funding from the Lustgarten Foundation, the National Institutes of Health and the Thompson Foundation. None of this work is related to the publication. T.U. and G.W.B. were consultants for Repare Therapeutics. G.W.B. received research support from Repare Therapeutics. The other authors report no competing interests.
Peer review
Peer review information
Nature Cancer thanks the anonymous reviewers for their contribution to the peer review of this work.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data
Extended Data Fig. 1 Generation of Cas9 stable cell lines and quality control analysis of CRISPR-Cas9 screen data.
a, b, Immunoblot analysis of Panc 08.13 and Panc 10.05 (panel a), and HPDE (panel b) pancreatic cell lines generated to stably express FLAG-Cas9. GAPDH or α-tubulin was used as a loading control. Data is representative of n = 3 independent immunoblots. c, Precision-recall curves for gene essentiality in the HPAF-II and Panc 08.13 CRISPR-Cas9 screens with gemcitabine. Gene essentiality for each screen was determined by calculating the Bayes Factor, and then compared to reference nonessential and essential gene training sets from Kim and Hart (2021) to determine how well each screen performed. d, e, Total mapped sequencing reads for each of the CRISPR-Cas9 screens performed. Dashed line indicates the expected total read count if 200-fold coverage of the sgRNA library was achieved (14,139,400 total reads). f–i, Read counts of sgRNAs targeting non-essential (panels f and g) or essential (panels h and i) genes in each CRISPR-Cas9 screen. Non-essential and essential genes were classified using the gene lists by Kim and Hart (2021). Center lines represent the median and box limits indicate the 25th and 75th percentiles of each sample. Whiskers extend 1.5x the interquartile range and individual data points indicate outliers.
Extended Data Fig. 2 Assessing APOBEC3C- and APOBEC3D-mediated gemcitabine resistance in pancreatic cell lines and organoids.
a, Heatmap of the viability of A3C- and A3D-deficient pancreatic cancer (HPAF-II, Panc 08.13, Panc 10.05, AsPC-1, and BxPC-3) and non-transformed cell lines (HPDE, HPNE-hTERT, and RPE1-hTERT p53−/−) following gemcitabine treatment. Cell viability was assessed following seven days of gemcitabine treatment using CellTiter-Glo. Gemcitabine sensitivity for each A3C- or A3D-deficient cell line was determined by normalizing cell viability to that of the same cell line expressing a control sgRNA (sgLacZ or sgAAVS1) and is indicated on the heatmap. n = 3 independent transductions and cell viability experiments. b-e, Quantification of A3C and A3D mRNA levels in HPAF-II (panel b), HPDE-Cas9 (panel c), HPNE-hTERT (panel d), and RPE1-hTERT-Cas9 p53−/− (panel e) A3C- and/or A3D-deficient cells by RT-qPCR analysis. A3C and A3D mRNA levels in each CRISPR-Cas9 knockout cell population was determined by comparing to the expression levels of each respective gene in the wildtype cell line. n = 3 independent RT-qPCR experiments, with bars indicating the medians. f, Immunoblot analysis of hT81 and hT82 pancreatic cancer organoids generated to stably express FLAG-Cas9. α-tubulin was used as a loading control. Data is representative of n = 3 independent immunoblots. g, Representative micrographs of hT81-Cas9 and hT82-Cas9 organoids following transduction with lentivirus co-expressing sgRNAs targeting A3D or A3C and eGFP to assess transduction efficiency prior to performing cell viability experiments. Scale bars, 100 µm. Data is representative of n = 3 independent transductions. h, i, Relative A3C and A3D mRNA levels in A3C- or A3D-deficient hT81-Cas9 (panel h) and hT82-Cas9 (panel i) pancreatic cancer organoids in comparison to each wildtype organoid population expressing sgLacZ. Bars represent the medians (n = 3 independent transductions and RT-qPCR experiments).
Extended Data Fig. 3 Nab-paclitaxel increases gemcitabine sensitivity in PDAC cells lacking APOBEC3C or APOBEC3D.
a, Quantification of multinucleated HPAF-II cells following 50 or 100 nM nab-paclitaxel treatment for 72 hours. A minimum of 1691 cells were quantified for untreated (n = 1691) and 50 nM (n = 1709) and 100 nM (n = 1734) nab-paclitaxel conditions, with greater than 411 cells quantified for each sample per experiment (n = 3 independent drug treatments and multinucleated cell counts). Statistical support was determined using two-tailed unpaired t-tests. Horizontal bars indicate the means. b, Representative micrographs of HPAF-II cells treated with 0, 50, or 100 nM nab-paclitaxel in the experiments from panel a, with multinucleated cells labeled with arrowheads. Scale bar, 10 µm. a, b, Relate to Fig. 2k.
Extended Data Fig. 4 APOBEC3C and APOBEC3D expression levels are correlated and induced by the innate immune response and following DNA damage and replication stress.
a, mRNA expression levels of A3C and A3D in pancreatic tumors from the TCGA-PAAD project (n = 183). Median expression level of each gene is indicated. PGK1, housekeeping gene. b, Scatterplot matrix comparing mRNA expression levels of each pairwise APOBEC3 combination in pancreatic tumors from the ICGC PACA-CA cohort and COMPASS clinical trial (n = 430). The bottom half of the matrix depicts individual pairwise APOBEC3 expression scatterplots, with regression lines plotted. Top half of the matrix indicates the one-tailed Pearson’s coefficient (r) for each pairwise combination. c, Induction of A3A, A3B, A3C, and A3D mRNA levels following 72 hour treatment of HPAF-II cells with a range of interferon β concentrations (0, 0.1, 1, or 10 ng/mL), analyzed by RT-qPCR. Data was normalized to ACTB expression, and the fold-change of each mRNA was determined by comparing to untreated cells. n = 3 independent RT-qPCR experiments. d–g, Immunoblot analysis of HPAF-II cells transduced with sgAAVS1 (control), sgIRF3 (panel d), sgSTAT1 (panel e), sgSTAT2 (panel f), or sgRELA (panel g). GAPDH was used as a loading control. Data is representative of n = 3 independent transductions and immunoblots. Relates to Fig. 3h. h, i, mRNA expression levels of A3C and A3D following treatment of Panc 08.13 (panel h) or Panc 10.05 (panel i) with the indicated DNA damaging and replication stress-inducing agents for 72 hours. Expression levels were measured by RT-qPCR and normalized to ACTB mRNA. Drug concentrations used were 500 nM gemcitabine, 5 µM cytarabine, 5 µM decitabine, 5 µM 5’azacytidine, 500 µM hydroxyurea, 1 µM cisplatin, 25 nM camptothecin, 2.5 µM AZD6738, 100 nM etoposide, 500 nM talazoparib, and 50 µM 5-fluorouracil. Cytidine analogues are indicated on each plot. Dashed line represents no expression level changes (fold-change of one). Horizontal bars represent the means (n = 2 independent RT-qPCR experiments).
Extended Data Fig. 5 APOBEC3C and APOBEC3D do not modulate gemcitabine resistance by activation of the innate immune response in pancreatic cancer cells.
a, Immunoblot analysis of phosphoSTAT1 Tyr701 in Panc 08.13 cells treated with 1 µM gemcitabine for 0, 24, 48, or 72 hours. α-tubulin was used as a loading control. Data is representative of n = 3 independent immunoblots. b, mRNA expression levels of interferon-stimulated genes (CCL5, ISG15, and ISG45) in HPAF-II-Cas9 cells transduced with a sgRNA targeting LacZ (control), A3C, or A3D following 500 nM gemcitabine treatment for 72 hours. Fold-change in mRNA expression of each interferon-stimulated gene in gemcitabine relative to untreated conditions is plotted. Horizontal bars represent the means (n = 3 independent transductions and RT-qPCR experiments). Statistical support was determined using two-tailed unpaired t-tests. n.s., not significant (p > 0.05).
Extended Data Fig. 6 Cytidine deaminase assays with APOBEC3A and APOBEC3C.
a, SDS-PAGE of purified recombinant A3A, A3C, and deaminase-dead A3CC97S/C100S from expression in insect cells, visualized with Coomassie Blue. Relates to Fig. 5b,c. b, Schematic of the PCR-based in vitro cytidine deaminase assay used to determine whether A3A or A3C can deaminate gemcitabine in ssDNA. Either of two ssDNA substrates (one containing a single deoxycytidine and other containing gemcitabine in place of the deoxycytidine) were incubated with A3A or A3C for 2 hours at 37 °C. DNA substrates from the in vitro reactions were then used as templates for PCR amplification, where deamination of deoxycytidine or gemcitabine introduces an MseI restriction enzyme site upon amplification that is cleaved to identify A3A or A3C deamination. Restriction enzyme digest products are then resolved on a nondenaturing gel to reveal cytidine deamination. Relates to Fig. 5b,c. c, Representative images of the restriction digest products generated from the in vitro cytidine deaminase assays using 0, 100, or 250 nM deaminase-dead (C97S/C100S) A3C. Data is representative of n = 3 independent in vitro assays. Relates to Fig. 5b.
Extended Data Fig. 7 APOBEC3C and APOBEC3D deaminate deoxycytidines in genomic DNA, but not intracellular deoxycytidine or gemcitabine nucleosides.
a, Stages of intracellular metabolism at which gemcitabine can be deaminated, leading to its inactivation. Gemcitabine can be deaminated at the free base level (dFdC) to generate dFdU, which is the major mode of gemcitabine inactivation within cells, but can also be deaminated in its monophosphorylated form (dFdCMP) and after it is incorporated into genomic DNA. b, Standard curve for deaminated gemcitabine bases by LC/MS to confirm the lack of dFdU in genomic DNA samples is not due to the sensitivity of the mass spectrometer. Relates to Fig. 5d. c, Quantification of gemcitabine bases in genomic DNA isolated from parental and A3C- and A3D-deficient HPAF-II cells following 500 nM or 1 µM gemcitabine treatment for 24 hours by LC/MS. Data is normalized to levels of 13C15N2-dFdC (heavy dFdC) spiked into each sample. Horizontal bars represent the means (n = 3 independent experiments). Significance was determined using two-tailed unpaired t-tests. d-j, Quantification of intracellular deoxycytidine (dC, dU, dCMP, dUMP, dCTP, and dUTP) and gemcitabine (dFdCMP) bases or nucleosides from parental and A3C- and A3D-deficient HPAF-II cells treated with 500 nM gemcitabine for 4 or 24 hours by LC/MS. The reduction in dU, dUMP, and dCTP levels following gemcitabine treatment has been previously described and are due to the inhibition of ribonucleotide reductase and thymidylate synthetase, respectively. n = 3 independent experiments, with horizontal bars representing the means. Statistical significance was assessed using two-tailed unpaired t-tests. k, Immunoblot analysis of HPAF-II cells engineered to disrupt the uracil DNA glycosylase gene UNG to allow for improved detection of nascent genomic uracils. α-tubulin was used as a loading control. l, Quantification of colony formation of HPAF-II-Cas9 cells transduced with sgLacZ (control), sgA3C, or sgA3D. Cells were treated with 1 μM hydroxyurea for 14 days before colonies were stained and counted. Horizontal bars indicate the means (n = 2 independent transductions and clonogenic survival assays).
Extended Data Fig. 8 APOBEC3C or APOBEC3D inactivation increases DNA replication stress in pancreatic cancer cells, but not in nontransformed cells.
a, Immunoblot analysis of RPA2 in HPAF-II-Cas9 cells transduced with sgLacZ (control), sgA3C, or sgA3D. Cells were treated with 1 µM gemcitabine for 24 hours and recovered for 0, 24, 48, or 72 hours prior to assessing replication stress response activation. α-tubulin was used as a loading control. Data is representative of n = 3 independent immunoblots. b, Quantification of chromatin-bound S phase RPA2 fluorescence intensity in parental and A3C- and A3D-deficient HPAF-II cells treated with 1 μM gemcitabine for 24 hours. n = 3 independent experiments. Relates to Fig. 6a. c-d, RPE1-hTERT-Cas9 p53−/− cells transduced with sgAAVS1 (control), sgA3C, or sgA3D were treated with 1 µM gemcitabine for 24 hours and chromatin-bound RPA2 fluorescence intensity was measured. A minimum of 21 000 cells were analyzed per sample. Center lines represent the median and box limits indicate the 25th and 75th percentiles of each sample. Whiskers extend 1.5x the interquartile range and individual data points indicate outliers. Quantification of each of the three independent experiments are shown in panel d. e, Median RPA2 intensity in RPE1-hTERT-Cas9 p53−/− cells transduced with sgAAVS1, sgA3C, or sgA3D following 24 hour treatment with 1 μM gemcitabine. n = 3 independent transductions and immunostainings. Circles with black outlines represent the median of each independent experiment and black bars represent the median and first and last quartiles of all experiments. Untreated: n = 988 (sgAAVS1), n = 1204 (sgA3C), n = 924 (sgA3D). Gemcitabine-treated: n = 791 (sgAAVS1), n = 808 (sgA3C), n = 749 (sgA3D). A minimum of 216 cells were measured per experimental sample. f, Analysis of 53BP1 nuclear bodies in EdU-negative parental and A3C- and A3D-deficient HPAF-II cells following 24 hour treatment with 1 µM gemcitabine. Circles with black outlines represent the median of each experiment and black bars represent the median and first and last quartiles of all experiments (n = 3 independent immunostainings). Untreated: n = 1558 (parental), n = 1831 (A3C-deficient), n = 1605 (A3D-deficient). Gemcitabine-treated: n = 2113 (parental), n = 1784 (A3C-deficient), n = 1983 (A3D-deficient). A minimum of 316 cells were measured per experiment. Two-tailed unpaired Mann-Whitney U tests were used to determine statistical significance and asterisks (***) indicate a p-value < 0.001. g, Analysis of fluorescence intensity of chromatin-bound 53BP1 in parental and A3C- and A3D-deficient HPAF-II G1 cells treated with 1 μM gemcitabine for 24 hours. n = 3 independent experiments. Relates to Fig. 6b. h, Representative flow cytometry plots for EdU incorporation in parental and A3C- and A3D-deficient HPAF-II cells following treatment with 1 μM gemcitabine for 24 hours. Relates to Fig. 6d. i, Percentage of parental and A3C- and A3D-deficient HPAF-II cells in each cell cycle phase following treatment with 1 µM gemcitabine for 24 hours. n = 3 independent experiments. Bars for each cell cycle stage represent the means of the independent experiments, with the error bars showing the standard deviation. j, DNA combing analysis of RPE1-hTERT-Cas9 p53−/− cells transduced with sgAAVS1, sgA3C, or sgA3D. Cells were pulsed with CldU for 30 minutes, followed by a pulse with IdU for 30 minutes in the presence or absence of 500 nM gemcitabine. Untreated: n = 251 (sgAAVS1), n = 240 (sgA3C), n = 280 (sgA3D). Gemcitabine-treated: n = 232 (sgAAVS1), n = 209 (sgA3C), n = 259 (sgA3D). Statistical support was assessed using a two-tailed unpaired Mann-Whitney U test. k, Replication fork restart assay in control (sgAAVS1) and A3C- and A3D-deficient RPE1-hTERT-Cas9 p53−/− cells. Cells were pulsed with CldU for 30 minutes, followed by treatment with 1 μM gemcitabine for 30 minutes and a 30 minute IdU pulse in drug-free media to assess the ability of replication forks to restart DNA synthesis following gemcitabine. Quantification of elongated and stalled replication forks upon gemcitabine recovery is displayed. A total of n = 538 (sgAAVS1), n = 508 (sgA3C), and n = 480 (sgA3D) replication tracks were quantified per sample, with a minimum of 200 replication tracks analyzed for each experiment (n = 2 independent transductions and replication fork restart assays).
Extended Data Fig. 9 Analysis of base excision repair-, RAD51-, and translesion synthesis-mediated replication fork restart following gemcitabine treatment.
a, Immunoblot analysis of HMCES in HPAF-II-Cas9 cells transduced with sgAAVS1 (control) or sgHMCES. α-tubulin was used as a loading control. Data is representative of n = 3 independent transductions and immunoblots. b, Viability of HPAF-II-Cas9 cells transduced with sgAAVS1 or sgHMCES and treated with a range of gemcitabine concentrations for 72 hours. CellTiter-Glo was used to measure cell viability. n = 3 independent transductions and cell viability experiments. Circles represent the means of the experiments and error bars indicate the standard deviations. c, Replication fork restart assay in wildtype and HMCES-deficient HPAF-II cells. Cells were pulsed with CldU for 30 minutes, followed by treatment with 1 μM gemcitabine for 30 minutes and a 30 minute IdU pulse to assess the ability of replication forks to restart DNA synthesis. A total of n = 585 (wildtype), n = 510 (HMCES-1-deficient), and n = 593 (HMCES-2-deficient) replication tracks were quantified for each sample, with a minimum of 229 replication tracks analyzed per experiment (n = 2 independent transductions and replication fork restart assays). Quantification of elongated and stalled replication forks upon gemcitabine recovery is plotted. Horizontal bars indicate the means. d, Viability analyses of HPAF-II-Cas9 cells transduced with sgLacZ (control), sgSMUG1, sgAPEX1, or sgAPEX2 and treated with the indicated gemcitabine concentrations for 72 hours, measured by CellTiter-Glo. n = 3 independent transductions and cell viability experiments, with circles and error bars representing the means and standard deviations. e, Representative micrographs of EdU-positive RAD51 foci in parental and A3C- and A3D-deficient HPAF-II cells following 1 µM gemcitabine treatment quantified in Fig. 6i. Scale bars, 10 µm. f, Median number of S phase RAD51 foci in each of the three independent immunostainings performed in Fig. 6i. g, Analysis of S phase (EdU-positive) RAD51 foci in parental and A3C- and A3D-deficient HPAF-II cells following 5 µM mitomycin C treatment for 24 hours. The number of RAD51 foci per cell are plotted, where circles with black outlines indicate the median of each experiment and black bars represent the median and first and last quartiles of all three experiments (n = 3 independent immunostainings). Untreated: n = 1787 (parental), n = 1073 (A3C-deficient), n = 1058 (A3D-deficient). Mitomycin C: n = 1044 (parental), n = 718 (A3C-deficient), n = 636 (A3D-deficient). A minimum of 201 cells were analyzed per experiment. Statistical support was assessed using a two-tailed unpaired Mann-Whitney U test. n.s., not significant (p > 0.05). h, Quantification of S phase (EdU-positive) 53BP1 foci in parental and A3C- and A3D-deficient HPAF-II cells following 1 µM gemcitabine for 24 hours. Circles with black outlines represent the median of each experiment and black bars represent the median and first and last quartiles of all three experiments (n = 3 independent immunostainings). Untreated: n = 843 (parental), n = 847 (A3C-deficient), n = 761 (A3D-deficient). Gemcitabine: n = 935 (parental), n = 960 (A3C-deficient), n = 847 (A3D-deficient). Two-tailed unpaired Mann-Whitney U tests were used to determine statistical significance and asterisks (***) indicate a p-value < 0.001. n.s., not significant (p > 0.05). i, Immunoblot analysis of POLH in HPAF-II-Cas9 cells transduced with sgAAVS1 or sgPOLH. α-tubulin was used as a loading control. Data is representative of n = 3 independent transductions and immunoblots. Related to Fig. 6j,k.
Supplementary information
Supplementary Figure
Supplementary Fig. 1.
Supplementary Table
Supplementary Tables 1–10.
Source data
Source Data Fig. 1
Numerical source data.
Source Data Fig. 2
Numerical source data.
Source Data Fig. 3
Numerical source data.
Source Data Fig. 4
Numerical source data.
Source Data Fig. 5
Numerical source data.
Source Data Fig. 6
Numerical source data.
Source Data Fig. 7
Numerical source data.
Source Data Extended Data Fig. 1
Numerical source data.
Source Data Extended Data Fig. 2
Numerical source data.
Source Data Extended Data Fig. 3
Numerical source data.
Source Data Extended Data Fig. 4
Numerical source data.
Source Data Extended Data Fig. 5
Numerical source data.
Source Data Extended Data Fig. 7
Numerical source data.
Source Data Extended Data Fig. 8
Numerical source data.
Source Data Extended Data Fig. 9
Numerical source data.
Source Data All Figures
Unprocessed gels and immunoblots for all main figures and extended data figures.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Ubhi, T., Zaslaver, O., Quaile, A.T. et al. Cytidine deaminases APOBEC3C and APOBEC3D promote DNA replication stress resistance in pancreatic cancer cells. Nat Cancer (2024). https://doi.org/10.1038/s43018-024-00742-z
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s43018-024-00742-z
