STAT3 imparts BRCAness by impairing homologous recombination repair in Epstein-Barr virus-transformed B lymphocytes

Abstract

Epstein-Barr virus (EBV) causes lymphomas and epithelial cell cancers. Though generally silent in B lymphocytes, this widely prevalent virus can cause endemic Burkitt lymphoma and post-transplant lymphoproliferative disorders/lymphomas in immunocompromised hosts. By learning how EBV breaches barriers to cell proliferation, we hope to undermine those strategies to treat EBV lymphomas and potentially other cancers. We had previously found that EBV, through activation of cellular STAT3 prevents phosphorylation of Chk1, and thereby, suppresses activation of the intra-S phase cell-cycle checkpoint, a potent barrier to oncogene-driven proliferation. This observation prompted us to examine the consequences on DNA repair since homologous recombination repair, the most error-free form, requires phosphoChk1. We now report that the defect in Chk1 phosphorylation also curtails RAD51 nucleation, and thereby, homologous recombination repair of DNA double strand breaks. The resulting reliance on error-prone microhomology-mediated end-joining (MMEJ) repair makes EBV-transformed cells susceptible to PARP inhibition and simultaneous accrual of genome-wide deletions and insertions resulting from synthesis-dependent MMEJ. Analysis of transcriptomic and drug susceptibility data from hundreds of cancer lines reveals a STAT3-dependent gene-set predictive of susceptibility of cancers to synthetic lethal PARP inhibition. These findings i) demonstrate how the tumor virus EBV re-shapes cellular DNA repair, ii) provide the first genome-wide evidence for insertions resulting from MMEJ in human cells, and iii) expand the range of cancers (EBV-related and -unrelated) that are likely to respond to synthetic lethal inhibitors given the high prevalence of cancers with constitutively active STAT3.

Fig 1. EBV-infected/transformed proliferating B cells with functional STAT3 demonstrate scarce RAD51 foci-containing nuclei.

(A and B) Primary B lymphocytes from healthy subjects and patients with Job’s syndrome were infected with EBV and placed in culture for 4 days. Representative immunofluorescence images of nuclei stained with DAPI and for EBNA2 and costained for RAD51 are shown in A. Aggregate data from 100 EBNA2⁺ nuclei each from healthy and Job’s cells are shown in B. Table in C shows percent infected cells in S phase on day 4; cell cycle profiles of representative healthy and Job’s samples are shown on the right. (D and E) Two healthy subject-derived EBV-transformed cell lines (LCL) were transfected with siRNA to STAT3 or scrambled (Sc) siRNA and harvested 36h later. Aggregate data from immunofluorescence images of >100 nuclei stained with DAPI and costained for ATR or RAD51 are shown in D. Cells were subjected to immunoblotting for STAT3 and β-actin in E. (F and G) Bleomycin-treated LCL derived from 3 healthy subjects and 3 Job’s syndrome patients were enumerated for live cells using Trypan blue staining on indicated days and percent recovery calculated by comparing to matched Bleomycin-untreated LCL (F). Immunofluorescence images of representative Bleomycin-exposed LCL nuclei that were costained for DAPI and γH2AX are shown in G; error bars indicate SEM in B, D, and F.

Fig 2. STAT3 restricts HR repair through Chk1 in EBV-transformed cells.

(A-K) LCL derived from a healthy subject (A-E, K) and EBV-positive HH514-16 Burkitt lymphoma (BL) cells (F-J, K) were transfected with DR-GFP plasmid (A-D, F-I) and empty vector pCAGGS (A, F) or ISce1 plasmid (B-D, G-I), treated with 25μM (C, H) or 50μM (D, I) AG490 after 18h, and harvested after another 30h for analysis of GFP-positive cells by flow cytometry (A-D, F-I) and immunoblotting for phospho(p)STAT3 and β-actin (K). LCL (E) and BL cells (J) were transfected in parallel with pEGFP to monitor transfection efficiency. (L-O) BL cells with stably-integrated DR-GFP were transfected with Chk1 plasmid (wild-type [L-N] or S345A mutant [O]) and pCAGGS (L) or ISce1 plasmid (M-O), treated with 50μM AG490 after 18h, and harvested after another 30h for analysis of GFP-positive cells by flow cytometry. Numbers in plots indicate percent GFP-positive cells; both side scatter (A-J) and empty channel (L-O) lack fluorescence staining. Experiments were performed 3 times.

Fig 3. EBV-transformed cells are susceptible to PARP inhibition and demonstrate MMEJ-mediated DSB repair.

(A-I) LCL derived from three healthy subjects (A-C and G-I) and three EBV⁺ BL cell lines (HH514-16, Akata, and Raji; D-F) were grown in the presence of Olaparib (A-F) or Veliparib (G-I) and enumerated for live cells on indicated days; PARP inhibitors were added at time 0 and every 3–4 days thereafter at indicated concentrations. (J) EBV⁺ BL cell lines (HH514-16, Akata, and Raji) were left untreated (upper panels) or treated with 0.5μM Olaparib every 3–4 days (lower panels), harvested on day 8, and assayed for dead cells using propidium iodide (PI) staining and flow cytometry. (K-R) LCL (K-N) and HH514-16 BL cells (O-R) were transfected with DR-GFP plasmid (K, L, O, P) and empty vector pCAGGS (K, O) or ISce1 plasmid (L, P) versus EJ2 plasmid (M, N, Q, R) and pCAGGS (M, Q) or ISce1 plasmid (N, R) and harvested after 48h for analysis of GFP-positive cells by flow cytometry. Numbers in plots indicate percent GFP-positive cells. Experiments were performed 3 times.

Fig 4. EBV-transformed cells demonstrate newly-generated deletions and insertions bearing signatures of MMEJ.

Two-week old LCL derived from 2 healthy subjects and their respective primary B lymphocytes were subjected to whole genome sequencing. (A, D) Newly-generated versus pre-existing deletions of different lengths bearing MMEJ-signatures are shown in A with an example of one such deletion shown in D. (B, E) Newly-generated versus pre-existing small insertions bearing signatures of synthesis-dependent MMEJ are tabulated on the left with mean insertion sizes on the right in B. An example of such an insertion with flanking regions of microhomology (mh1, mh2, P1, and P2) is shown in E; the inserted nucleotide is boxed. In this example, looping-out of the top strand and mispriming of P2 on P1 of the bottom strand is followed by insertion of a single nucleotide (G) and templated synthesis of mh2; the strands then separate and resume DNA synthesis following realignment of P1, mh1, P2, and mh2 at the appropriate regions on the complementary strand. (C, F) Newly-generated versus pre-existing large insertions bearing signatures of snapback synthesis MMEJ are tabulated on the left with mean insertion sizes on the right in C. Two examples of such large insertions are shown in F. The top sequence is an example in which there is likely to have been templated synthesis through a snapback mechanism on the same strand generating 35 nucleotides of inverted repeats (underlined) resulting in an 84 nucleotide insertion. The lower sequence is a 130 nucleotide insertion in which there were multiple snapback events resulting in two sets of inverted repeats of 7 nucleotides each (numbered 1–4). #2 resulted from using #1 as a template or another 7-mer matching #1 in the original sequence. Similarly, #3 resulted from using #2 (or another 7-mer matching #2 in the original sequence) and #4 resulted from using #1 or #3 as a template (or another matching 7-mer in the original sequence). The intervening sequences likely arose from a combination of non-templated insertions and insertions templated from complementary regions in the original sequence.

Fig 5. Cross-analysis between STAT3-targetome, gene expression, and PARP inhibitor susceptibility data in cancer lines from a range of tissues identifies a gene signature that predicts susceptibility to PARP inhibition.

(A) Mean-difference plot showing differential expression of STAT3 transcriptional targets between cancer lines with highest sensitivity (corresponding to ~30% of sensitive lines) and those with highest resistance (corresponding to ~10% of resistant lines) to a PARP inhibitor. Red spots represent 699 genes with significantly higher expression in highly sensitive lines, green spots correspond to 472 genes demonstrating higher expression in highly resistant lines, and black spots represent 5899 genes that were not differentially expressed. (B and C) Shown in B is a hierarchically clustered binary plot of expression of 27 (of 699) genes with higher expression in all lines with high sensitivity to PARP inhibitor; high or low calls were based on whether expression exceeded the sensitive mean minus one standard deviation. A second binary plot, derived from the plot in B, displayed on an IC50 scale using the subpopulation of lines (indicated by a yellow bar in B) that expressed overall high levels of the 27 genes is shown in C. Examination of this binary plot led to the selection of nine genes with high expression in lines with low IC50s (i.e. in sensitive lines) but low expression in lines with high IC50s (i.e. in resistant lines). Two additional genes found to be good predictors of IC50 based on independent Lasso and Elastic net analyses of STAT3-transcriptional targets were also among the 27 genes from above. These were added to the nine genes to yield an 11-gene signature, shown in D. (E and F) ROC curves derived from applying the 11-gene signature to experimental data on gene expression and susceptibility to PARP inhibitors in all cancer lines (>450 from a variety of tissue types; E) versus blood cancer lines (F) within the Cancer Genome Project dataset; AUC, Area under the ROC Curve.