Loss of TGFβ signaling increases alternative end-joining DNA repair that sensitizes to genotoxic therapies across cancer types

Abstract

Among the pleotropic roles of transforming growth factor-β (TGFβ) signaling in cancer, its impact on genomic stability is least understood. Inhibition of TGFβ signaling increases use of alternative end joining (alt-EJ), an error-prone DNA repair process that typically functions as a "backup" pathway if double-strand break repair by homologous recombination or nonhomologous end joining is compromised. However, the consequences of this functional relationship on therapeutic vulnerability in human cancer remain unknown. Here, we show that TGFβ broadly controls the DNA damage response and suppresses alt-EJ genes that are associated with genomic instability. Mechanistically based TGFβ and alt-EJ gene expression signatures were anticorrelated in glioblastoma, squamous cell lung cancer, and serous ovarian cancer. Consistent with error-prone repair, more of the genome was altered in tumors classified as low TGFβ and high alt-EJ, and the corresponding patients had better outcomes. Pan-cancer analysis of solid neoplasms revealed that alt-EJ genes were coordinately expressed and anticorrelated with TGFβ competency in 16 of 17 cancer types tested. Moreover, regardless of cancer type, tumors classified as low TGFβ and high alt-EJ were characterized by an insertion-deletion mutation signature containing short microhomologies and were more sensitive to genotoxic therapy. Collectively, experimental studies revealed that loss or inhibition of TGFβ signaling compromises the DNA damage response, resulting in ineffective repair by alt-EJ. Translation of this mechanistic relationship into gene expression signatures identified a robust anticorrelation that predicts response to genotoxic therapies, thereby expanding the potential therapeutic scope of TGFβ biology.

Fig. 1.. Blockade of TGFβ signaling disrupts DDR and increases alt-EJ.

(A) SAS cells were treated with radiation (5 Gy, 1 hour), LY2157299 (2 μM, 24 hour), or combination, and cell lysates were prepared for proteomic analysis. Protein phosphorylation analyses was performed using two targeted, multiple reaction monitoring mass spectrometry (MRM-MS)-based assays (tables S1-S2). Representative phospho-analytes are plotted in the figure, including ATM phosphorylation at Ser2996, NBN phosphorylation at Ser343, and BRCA1 phosphorylation at Ser1524 quantified using the two independent assay panels that gave comparable results as shown in lower left and right panels. Precise quantification of the phospho-analytes relative to stable isotope labeled spiked-in standards are shown as peak area ratios. Fold changes of these proteins between irradiated cells and LY2157299 pretreated and irradiated cells are indicated. Data shown as means ± SEM of n = 3. Experiment was performed once. (B) Protein expression and phosphorylation in SAS cells treated with IR (5 Gy), LY2157299, or combination of both. Unsupervised clustering of Z-score data is shown as a heatmap. Representative proteins that are reciprocally regulated are indicated in red box and protein phosphorylations increased by LY2157299 are shown in blue box. (C) The frequency of HR measured by flow cytometry using reporter plasmid-transfected SAS cells that expressed miR-182, anti-miR-182, or scramble miRNA, and were treated with or without TGFβ receptor inhibitor LY2157299. (D) Alt-EJ repair frequency measured by flow cytometry of EJ2GFP reporter transfected SAS cells expressing anti-miR-182 or scrambled anti-miR and treated with or without LY2157299. (E) DNA repair efficiency measured by the PFGE assay after irradiation (IR, 20 Gy) of SAS cells pre-treated with DNA-dependent protein kinase inhibitor KU57788, TGFβ inhibitor LY364947, or both. Percentages of residual DNA damage at the indicated time points after IR are shown. Statistical significance is indicated according to Student’s t-test: *, P < 0.05; **, P < 0.01; ***, P < 0.005; n.s., P > 0.05.

Fig. 2.. TGFβ signaling regulates DDR gene expression.

(A) Gene expression measured in SAS cells that were treated with TGFβ, LY2157299, or combination of both for 24 hours using the NanoString panel. Unsupervised clustering of Z-score gene expression values is shown as a heatmap. Alt-EJ genes LIG1, PARP1, and POLQ are indicated. (B) Percentage of TGFβ-induced gene expression change versus LY2157299-induced gene expression, normalized to control, for SAS cells. Genes reciprocally regulated by TGFβ or LY2157299, including ABL1, CCND2, CDKN1A, LIG1, PARP1, POLD4, and POLQ, are indicated by red dots. (C) Percentage of TGFβ-induced gene expression in SAS cells overexpressing (OE) anti-miR-182 versus LY2157299-induced gene expression, normalized to control samples. Genes reciprocally regulated by TGFβ or LY2157299 independent of miR-182 are indicated by red dots. (D-E) qRT-PCR of POLQ, PARP1, and LIG1 in SAS cells treated with TGFβ (D) or LY2157299 (E) for 72 hours, normalized to untreated control. (F) Changes in gene expression in U251 GBM cells treated with 2 μM LY2157299 for 24 hours as measured using the NanoString panel. (G-H) Gene expression of PARP1, LIG1, and POLQ measured by qRT-PCR in U251 cells treated with TGFβ (G) or LY2157299 (H) for 72 hours. (I) Alt-EJ repair event frequency measured by EJ2GFP reporter in U251 cells in which TGFβ signaling was inhibited with either LY2157299 or LY364947. (J) Alt-EJ repair event frequency measured by EJ2GFP reporter in U251 cells transfected with anti-miR-182 or scramble anti-miR and treated with or without LY2157299, normalized to untreated control. Two-tailed Student’s t-test; *, P < 0.05; **, P < 0.01; ***, P < 0.005.

Fig. 3.. TGFβ and alt-EJ gene expression signatures are anti-correlated in HNSC and GBM.

(A) Schematic illustration of compiled signatures for TGFβ-induced and alt-EJ-linked genes. The TGFβ signature was established from MCF10A cells that were treated with TGFβ or LY364947 for seven days. The 36 gene alt-EJ signature was curated from the literature (11, 33). (33)(B) Unsupervised clustering of TCGA HNSC primary tumors based on the expression profiles of all genes included in the alt-EJ signature. The dataset included 243 HPV-negative and 36 HPV-positive (red bars) cases. The HPV-positive ones were clustered by high expression of alt-EJ genes. (C) Unsupervised clustering of HNSC based on the ssGSEA scores of the alt-EJ or TGFβ signatures. HPV positivity indicated in red. The two signatures are significantly anti-correlated (PCC = −0.42, P < 0.00001). (D) Heatmap based on unsupervised clustering of ssGSEA scores for alt-EJ and TGFβ signatures in the TCGA GBM microarray dataset; IDH1 mutation (black) and MGMT methylation status (green) are indicated. The two signatures are significantly anti-correlated (PCC = −0.35, P < 0.00001).

Fig. 4.. TGFβ and alt-EJ signature status associated with differential clinical outcomes after genotoxic therapy.

(A) Negative correlation of TGFβ and alt-EJ scores of TCGA GBM cases excluding the neural samples (PCC = −0.35, P < 0.00001); orange dots indicate low βAlt score tertile and blue dots indicate high βAlt score tertile, here and in E and I. (B) Fraction of genomic alterations as a function of βAlt score tertiles (Mann Whitney test P < 0.0001). (C-D) Kaplan-Meier graphs corresponding to the (C) OS (P = 0.096) or (D) PFS (P = 0.031) for subpopulations of patients with GBM treated with chemoradiation classified by βAlt score tertiles as shown in panel A. (E) Negative correlation of TGFβ and alt-EJ scores of TCGA LUSC cases (PCC = −0.43, P < 0.00001). (F) Fraction of genomic alterations as a function of βAlt score tertiles (Mann Whitney test P < 0.0001). (G-H) Kaplan-Meier graphs corresponding to the (G) OS (P = 0.05) or (H) PFS (P = 0.02) for subpopulations of patients with LUSC treated with chemotherapy and/or radiotherapy classified by βalt score tertiles as shown in panel E. (I) OVCA tumors exhibit a negative correlation of the two signatures (PCC = −0.32, P < 0.00001). (J) Fraction of genomic alterations as a function of βAlt score tertiles for OVCA tumors (Mann Whitney test, P < 0.001). (K-L) Kaplan-Meier graphs corresponding to the (K) OS (P = 0.004) or (L) PFS (P = 0.0027) for TCGA patients with OVCA in subpopulations classified by βAlt score tertiles as shown in panel I.

Fig. 5.. Pan-cancer analysis shows that TGFβ and alt-EJ gene expression are anti-correlated and associated with genomic alterations.

(A) Gene co-expression analyses for TGFβ and alt-EJ signature genes across solid tumors in TCGA database. Major clusters containing most of the alt-EJ and TGFβ signature genes are indicated: cluster A contains 27/32 (85%) of the alt-EJ signature genes, and cluster B contains 32/33 (97%) of the TGFβ signature genes. (B) Forest plot showing the PCC and 95% confidence interval (CI) in each cancer type (numbers of tumors included in each setting are indicated). A non-significant, negative PCC corresponds to pancreatic adenocarcinoma (PAAD, gray bar). (C) Forest plot showing the PCC and 95% CI for cell lines of each cancer type (numbers of each are indicated). Non-significant PCCs are indicated by gray bars. (D) Heatmap showing the PCC for each indel (ID) pattern versus the TGFβ and alt-EJ signatures. The signatures are reciprocally associated with ID6, ID10, and ID13.

Fig. 6.. Pan-cancer βAlt signature status associates with clinical outcomes after genotoxic therapy.

(A) Negative correlation of TGFβ and alt-EJ scores of TCGA cases treated with RT (PCC = −0.234, P < 0.0001). Symbols indicate βAlt low (orange) and βAlt high (blue) tertiles here and in C. (B) Kaplan-Meier graphs corresponding to the OS subpopulations classified by βalt score tertiles as shown in panel A. The HR, 95% CI, cases (n) included in the analysis, and log-rank test P value are shown. (C) Negative correlation of TGFβ and alt-EJ scores of TCGA cases treated with RT and/or ChT (PCC = −0.159, P < 0.0001). (D) Kaplan-Meier graphs corresponding to the OS of subpopulations classified by βAlt score tertiles as shown in panel C. The HR, 95% CI, cases (n) included in the analysis, and log-rank test P value are shown.