Harnessing transcriptional regulation of alternative end-joining to predict cancer treatment

Abstract

Alternative end-joining (alt-EJ) is an error-prone DNA repair pathway that cancer cells deficient in homologous recombination rely on, making them vulnerable to synthetic lethality via inhibition of poly(ADP-ribose) polymerase (PARP). Targeting alt-EJ effector DNA polymerase theta (POLθ), which synergizes with PARP inhibitors and can overcome resistance, is of significant preclinical and clinical interest. However, the transcriptional regulation of alt-EJ and its interactions with processes driving cancer progression remain poorly understood. Here, we show that alt-EJ is suppressed by hypoxia while positively associated with MYC (myelocytomatosis oncogene) transcriptional activity. Hypoxia reduces PARP1 and POLQ expression, decreases MYC binding at their promoters, and lowers PARylation and alt-EJ-mediated DNA repair in cancer cells. Tumors with HIF1A mutations overexpress the alt-EJ gene signature. Inhibition of hypoxia-inducible factor 1α or HIF1A expression depletion, combined with PARP or POLθ inhibition, synergistically reduces the colony-forming capacity of cancer cells. Deep learning reveals the anticorrelation between alt-EJ and hypoxia across regions in tumor images, and the predictions for these and MYC activity achieve area under the curve values between 0.70 and 0.86. These findings further highlight the critical role of hypoxia in modulating DNA repair and present a strategy for predicting and improving outcomes centered on targeting alt-EJ.

Graphical Abstract

Figure 1.

Cancer gene modules associated with HR status. (A) Graph showing the reconstruction error of the original TCGA BRCA tumor-by-gene matrix using consecutive sets (n) of NMF modules. The set with minimum error is indicated. (B) Scatter plots showing the Spearman correlation coefficient (r) and associated P between the predicted and observed HR-deficiency score (HRDsum) using the machine learning model. Left and right panels show the results of the training and test sets, respectively. (C) Average SHAP values of NMF gene modules, with modules 2 and 11 (highlighted with circles) showing the strongest positive and negative contributions to HRDsum prediction, respectively. (D) GSEA outputs of the alt-EJ (left panel) and TGFβ (right panel) signatures in modules 2 (top panel) and 11 (bottom panel). The GSEA normalized enrichment scores (NESs) and statistical significance (P) are indicated. (E) Histogram depicting the GSEA “Cancer Hallmarks” gene sets positively [false-discovery rate (FDR)-adjusted P < .05; NESs > 1.0; red bars] and negatively (FDR-adjusted P < .05; NESs < 1.0; green bars) associated with module 11. The MYC V1 and V2 gene sets correspond to curated MYC target sets from The Molecular Signatures Database.

Figure 2.

Hypoxia signaling anticorrelates with alt-EJ. (A) Forest plots showing the correlation (PCC and 95% CI) between the alt-EJ signature and HIF1A or the hypoxia 15-gene signature across various cancer types (study acronyms and tumor frequencies are displayed). Nominally significant correlations (P < .5) are indicated in the inset. (B) Plots of the TGFβ (left panel), hypoxia (middle panel), and alt-EJ (right panel) signature scores over time (pseudotime) in single cancer cells (A549, lung; DU145, prostate; MCF7, breast; and OVCA420, ovarian) exposed to TGFβ-1. The slopes from the numerical differentiation of the signatures are shown at the bottom. (C) Scatter plot showing the alt-EJ–TGFβ correlation (PCC) in TCGA KIRC tumors stratified by VHL status, as indicated in the inset. (D) Violin plot showing the overexpression of the alt-EJ signature in HIF1A mutated (MUT) relative to WT TCGA UCEC tumors. The significance (P) of the two-tailed Mann–Whitney test is indicated. (E) Violin plot showing the overexpression of the alt-EJ signature in HIF1A mutated (MUT) relative to WT TCGA pan-cancer (n = 19 cancer types). The significance (P) of the two-tailed Mann–Whitney test is indicated. (F) GSEA outputs of the alt-EJ signature in HCT116 HIF1A WT (left panel) and HIF1A-deteled (right panel) cells exposed to hypoxia versus normoxia. The GSEA–NESs and P-values are indicated. (G) Left panel, normalized activity of alt-EJ as measured by the EJ2-GFP reporter in UOS cells grown in hypoxia relative to hypoxia and exposed to PX-478, as indicated in the inset (n = 4 independent assays). The significance (P) of the one-way ANOVA test with Tukey correction is indicated. There are also indicated the P-values of the two-tailed Student’s paired-samples t-test. Right bottom panel, representative western blot results of PARylation in the above conditions. (H) Top-left panel, representative western blot results of PARylation in the following four UOS cell assays: normoxia; normoxia plus exposure to PX-478; hypoxia; and hypoxia plus exposure to PX-478, as indicated in the inset. Bottom left panel, quantification of PARylation relative to loading control in three independent assays. The significance of the two-tailed Student’s paired-samples t-test relative to normoxia is indicated. Bottom right panel, quantification of alt-EJ as measured by the EJ2-GFP reporter in UOS cells grown in normoxia relative to normoxia and exposed to PX-478.

Figure 3.

Interplay between hypoxia and MYC regulates alt-EJ. (A) Forest plots showing the correlation (PCC and 95% CI) between the alt-EJ signature and MYC expression (left panel) or the MYC-driven signature (right panel) across various cancer types (study acronyms and tumor frequencies are indicated). Nominally significant correlations (P < .05) are indicated in the inset. (B) Violin plot showing MYC-binding (fold change) at the promoter regions (±5 kb from transcription start site) of alt-EJ genes, across tissue (inset). The results correspond to the tissue with ≥1 cell line (dots) with a significant MYC-binding at the alt-EJ promoter set (FDR-adjusted Fisher’s exact test P < .05). The cell lines with the highest MYC-binding in each tissue type are indicated. (C) Graph showing the time-course trends of the expression of the alt-EJ, hypoxia, MYC, and TGFβ signatures in MCF10A cells exposed to TGFβ-1 (500 pg/ml) for 0 to 6 h. The significance (P) of the difference in the hypoxia and TGFβ signature slopes relative to alt-EJ (top inset) and MYC (bottom inset) signature slopes are indicated. (D) Left panel, downregulation of PARP1 and POLQ1 (quantitative reverse transcription polymerase chain reaction (qRT-PCR) assays with specific primers and SYBR) in MCF10A cells cultured in hypoxia, as indicated in the inset. The bars show the mean ± standard deviation (s.d.), and the two-tailed Student’s unpaired-samples t-test P-values are indicated (n = 3 independent assays; 3 replicates/assay). Middle panel, overexpression of GLUT1, OCT4, TGFβ1, and VIM in MCF10A cells cultured in hypoxia, as indicated in the inset. Right panel, western blot showing overexpression of HIF1α in MCF10A cells under hypoxia. The loading control, α-tubulin (TUBA), is also shown. (E) Left panel, downregulation of PARP1 and POLQ1 (TaqMan assays) in MCF10A cells cultured under hypoxia, as indicated in the inset. The bars show the mean ±  s.d., and the two-tailed Student’s unpaired-samples t-test P-values are indicated (n = 2 independent assays; 4 replicates/assay). Middle panel, results for genes involved in HR or NHEJ pathways. Right panel, confirmation of overexpression of GLUT1 and OCT4 in the cells under hypoxia. (F) Left panel, downregulation of PARP1 and POLQ1 (qRT-PCR assays with specific primers and SYBR) in MCF10A cells cultured in normoxia and exposed to CoCl₂, as indicated in the inset. The bars show the mean ±  s.d., and the two-tailed Student’s unpaired-samples t-test P-values are indicated. Right panel, confirmation of GLUT1 overexpression in the cells exposed to CoCl₂. (G) Anticorrelation between the hypoxia signature and PARP1 protein expression in the GDSC and CCLE datasets. The correlation (PCC) and corresponding significance (P) are indicated. (H) Reduced MYC binding at the promoters of the PARP1 and POLQ genes in MCF10A cells grown under hypoxia, as indicated in the inset. The results are shown as the MYC-binding fold change relative to normoxia, including control isotype immunoglobulin (IgG). The unpaired two-sided Student’s t-test P-values are indicated (n = 2 independent assays; 3 replicates/assay). (I) Violin plot showing the distributions of the alt-EJ and hypoxia signature scores (inset) in the tertiles of the ratio of the alt-EJ/TGFβ signature scores in TCGA cancer types (n = 19). Trends in the alt-EJ and hypoxia signature are denoted by dashed lines. The paired two-sided Student’s t-test P-values are indicated for the signature comparisons in each tertile of the alt-EJ/TGFβ ratio. Significance (P) of the ANOVA of the alt-EJ/TGFβ ratio and signature score terms and their interaction are also indicated (bottom inset).

Figure 4.

Synergy between HIF1α and PARPi in reducing cancer cell CFC. (A) Results of CFC assays in breast, ovarian, and prostate cancer cell lines exposed to vehicle (DMSO), rucaparib (2 μM), PX-478 (5 μM), and the combination of rucaparib and PX-478. The graphs show the mean ± s.d. The unpaired two-sided Student’s t-test P-values for the comparisons with the drug combination group are indicated. The Bliss synergy score (95% CI) is indicated at the bottom of each panel (Bliss score > 10 indicates synergism). (B) Drug dose matrix of PX-478 and rucaparib in cell viability assays. The color scale indicates synergy or antagonism, with the numbers in the cells denoting the percent difference in loss of viability compared with expected values assuming no synergy. (C) Left panel, western blot results of PARylation and HIF1α levels in DU145 cells exposed for 24 h to vehicle (DMSO), rucaparib (2 μM), PX-478 (25 μM), or the combination of the two drugs. The molecular weights, in kDa, are indicated. The results of the loading control (TUBA) are also shown. Right panel, quantification (arbitrary units, AU) of PARylation under previous conditions. The significance (P) of the two-tailed Mann–Whitney test is indicated (mean ±  s.d.; n = 4 independent assays). (D) Western blot analysis of PARylation and HIF1α levels in DU145 cells exposed for 24 h to vehicle (DMSO), olaparib (2 μM), and/or PX-478 (25 μM). PARylation and HIF1α levels are denoted in each condition as the ratio of the corresponding signal to the loading control (β-actin, ACTB), normalized to the basal condition without drug treatment (set as 1). (E) Left panel, western blot results of total PARylation in DU145 cells grown under normoxia or hypoxia. The results of the loading control (ACTB) are also shown. Right panel, quantification (AU) of PARylation under previous conditions. The significance (P) of the two-tailed Mann–Whitney test is indicated (mean ±  s.d.; n = 5 independent assays). (F) Graph showing the ChIP assays of MYC binding at the promoters of the PARP1 and POLQ genes in DU145 cells grown under normoxic or hypoxic conditions, as indicated in the inset. The results are shown as MYC-binding fold change relative to normoxia, including the control IgG. The unpaired two-sided Student’s t-test P-values are indicated (n = 2 independent assays; 3 replicates/assay).

Figure 5.

Synergy between HIF1α inhibition or HIF1A expression depletion and POLθ or PARPi in reducing cancer cell CFC. (A) CFC assays for vehicle (DMSO), novobiocin (100 μM), PX-478 (5 μM), and the combination of novobiocin and PX-478. The graphs indicate mean ± s.d. The unpaired two-sided Student’s t-test P-values for the comparisons with the drug combination group are indicated. The Bliss synergy score (95% CI) is indicated at the bottom of each panel (Bliss score > 10 indicates synergism). (B) Left panels, graphs showing the integrated density of phospho-Ser139 γH2AX signal per nucleus: top, PC3 cells; bottom, T47D cells. Data are presented as ≥ 100 nuclei per condition, with the mean value indicated by a horizontal line. The significance of the one-way ANOVA test is shown for all conditions relative to DMSO and for drug combinations relative to PX-478 alone. Right panels, representative images of phospho-Ser139 γH2AX immunodetection under the same conditions. The smaller images show the 4′,6-diamidino-2-phenylindole (DAPI) merge for nuclear staining. (C) Left panels, CFC assays for vehicle (DMSO), rucaparib (2 μM), and novobiocin (100 μM) in T47D cells transduced with pLKO or shHIF1A. Middle panel, semi-quantitative gene expression analysis of HIF1A in the assayed T47D cells; the graphs show the mean ± s.d. (n = 2 independent assays; 3 replicates/assay) relative to pLKO, and the PPIA gene was used as the control. The unpaired two-sided Student’s t-test P-value is indicated. Right panel, the graph shows the mean ± s.d. The unpaired two-sided Student’s t-test P-values for the comparisons with the vehicle are indicated. The Bliss synergy score (95% CI) is indicated at the bottom of each panel (Bliss score > 10 indicates synergism).

Figure 6.

Deep learning classification of alt-EJ and hypoxia status. (A) Left panels, representative images of the inferred alt-EJ and hypoxia signature scores in a GBM tissue. Right panel, scatter plot of the alt-EJ–hypoxia signature (standardized score) correlation in GBM tumors (n = 8). The corresponding PCCs are shown: five are significantly (P < .05) negative, two are not significant, and one is significantly positive. (B) Pathology image, and alt-EJ and hypoxia attention maps of a TCGA basal-like (PAM50-classified) breast tumor developed in a women carrier of a BRCA1 pathological variant (tumor ID: TCGA-AN-A0XU). (C) Volcano plot for PCCs between the alt-EJ and hypoxia or MYC signature attention scores in breast tumors correctly classified as “high-alt-EJ and high-hypoxia”, “high-alt-EJ and low-hypoxia”, or “high-alt-EJ and high-MYC”, as indicated in the inset. The tumors with “high-alt-EJ and high-hypoxia” tended to show alt-EJ-hypoxia anticorrelation.