Homologous recombination (HR)-deficient cancers are sensitive to poly-ADP ribose polymerase inhibitors (PARPi), which have shown clinical efficacy in the treatment of high-grade serous cancers (HGSC). However, the majority of patients will relapse, and acquired PARPi resistance is emerging as a pressing clinical problem. Here we generated seven single-cell clones with acquired PARPi resistance derived from a PARPi-sensitive TP53−/− and BRCA1−/− epithelial cell line generated using CRISPR/Cas9. These clones showed diverse resistance mechanisms, and some clones presented with multiple mechanisms of resistance at the same time. Genomic analysis of the clones revealed unique transcriptional and mutational profiles and increased genomic instability in comparison with a PARPi-sensitive cell line. Clonal evolutionary analyses suggested that acquired PARPi resistance arose via clonal selection from an intrinsically unstable and heterogenous cell population in the sensitive cell line, which contained preexisting drug-tolerant cells. Similarly, clonal and spatial heterogeneity in tumor biopsies from a clinical patient with BRCA1-mutant HGSC with acquired PARPi resistance was observed. In an imaging-based drug screening, the clones showed heterogenous responses to targeted therapeutic agents, indicating that not all PARPi-resistant clones can be targeted with just one therapy. Furthermore, PARPi-resistant clones showed mechanism-dependent vulnerabilities to the selected agents, demonstrating that a deeper understanding on the mechanisms of resistance could lead to improved targeting and biomarkers for HGSC with acquired PARPi resistance.
This study shows that BRCA1-deficient cells can give rise to multiple genomically and functionally heterogenous PARPi-resistant clones, which are associated with various vulnerabilities that can be targeted in a mechanism-specific manner.
PARP inhibitors (PARPi) are synthetically lethal in tumors that are deficient in homologous recombination (HR) DNA repair (1, 2). More than 50% of high-grade serous ovarian (HGSC), fallopian tube, or peritoneal cancers are deficient in HR most commonly via mutations in BRCA1 or BRCA2, or other genes of the Fanconi anemia pathway (1). PARPis have been shown to improve progression-free survival as first-line maintenance therapy (3, 4) and in the treatment of relapsed ovarian cancer (5–7). However, as PARPis are entering wide clinical use, patients with acquired PARPi resistance are becoming a predominant clinical challenge.
Extensive in vivo and in vitro preclinical studies have identified several resistance mechanisms to PARPis. These mechanisms can be classified into four main categories including those that (i) lead to HR restoration, (ii) influence replication fork protection, (iii) directly impact on the abundance and activity of PAR chains, and (iv) influence cellular availability of the inhibitor (reviewed in ref. 8). Importantly, resistance to platinum-based chemotherapies is a strong predictor of PARPi resistance, suggesting that they probably share common mechanisms (9). Restoration of BRCA1/2 protein expression is the best characterized mechanism of platinum agent resistance mediated by HR in HGSC, which can be caused either by a reversion of the original deleterious mutation in the gene (10, 11), or by gaining the expression of a hypomorphic protein (12). Downregulation of non-homologous end joining (NHEJ) factors, such as 53BP1 (13), and REV7 (14) and proteins of the SHIELDIN complex (15) has been shown to restore HR via promoting end resection at the DNA double-strand breaks, and thus contributing to PARPi resistance. Recently, emerging preclinical evidence has demonstrated that PARPi resistance can result from enhanced replication fork protection (16–19).
Previous studies on the mechanisms of PARPi resistance have been mostly performed in cell lines, derived from already drug-resistant patients' samples (20, 21), and only individual mechanisms of PARPi resistance have been described using these cell lines. On the other hand, patient-derived samples have led to the detection of only reversion mutations in HR genes as mechanisms of resistance (11, 22). Detailed features of PARPi resistance, such as functional heterogeneity, genomic patterns, and the evolutionary trajectories of PARPi resistance are unknown.
In an effort to further dissect the DNA repair mechanisms leading to PARPi resistance, we generated a TP53-deficient, BRCA1-deficient epithelial cell line model system. We herein show that BRCA1 deficiency gives rise to a chromosomally unstable cell population with intrinsic clonal heterogeneity, which can contain preexisting drug tolerant cells. Via clonal selection, these drug tolerant cells can give rise to multiple genomically and functionally heterogenous PARPi-resistant clones. The clonal evolutionary patterns are conserved in a clinical patient with HGSC with germline BRCA1− mutation and acquired PARPi resistance. Importantly, we show that PARPi resistance is associated with cell-cycle–related vulnerabilities, that can be targeted in a mechanism-dependent manner.
Materials and Methods
We generated TP53- and BRCA1-deficient cells from RPE-1 hTERT cells purchased from ATCC as described previously (23). The cells were cultured using DMEM/F12 medium supplemented with 10% FBS and 1% penicillin-streptomycin. The cells were regularly tested negative for Mycoplasma and authenticated using whole-exome sequencing (WES). The cells were treated cyclically with increasing concentrations of PARPi during 3 months (Supplementary Fig. S1A). After the generation of a resistant pool, single-cell clones were isolated and propagated for the characterization assays for maximum of 3 months or 10 passages (Supplementary Fig. S1B). Details of the in vitro methods including cell viability and clonogenic assays, Western blotting, immunofluorescence (IF), flow cytometry, and cytogenetics, are shown in Supplementary Materials and Methods and Supplementary Table S1.
DNA and RNA isolation and sequencing
The schematics and timelines of the sequencing is shown in Supplementary Fig. S1, and methods in Supplementary Materials and Methods. The RNA sequencing (RNA-seq) and WES data are publicly available in Synapse at doi:10.7303/syn22240938.
Differential gene expression analysis and pathway analysis
Details of the RNA-seq data analysis are shown in Supplementary Materials and Methods. Briefly, the multidimensional scaling (MDS) plots were generated using the plotMDS function of the limma R package (24). Differential gene expression analysis was performed using the edgeR package (25). Multiple testing correction to control the FDR was performed using the Benjamini–Hochberg procedure on the P values. Differentially expressed genes (DEG) were determined using an FDR of 2.5%. The gene set enrichment analysis (GSEA; ref. 26) was performed using the GSEAPreranked module of the GSEA Java application (gsea-v3.0.jar).
Details of the WES analysis are presented in Supplementary Materials and Methods. In brief, the sequences were aligned to hg38 using Burrows-Wheeler Aligner. Mutations were called using MuTect2 from GATK4.0.11 and copy-number variations (CNV) using ASCAT and GATK4.1.2. Evolutionary analysis was performed from mutations and CNVs using PyClone0.13 and ClonEvol (27). Only subclones whose frequency was above 1% and at least five mutations after filtering were included in the analysis. The WES, mutational calls, and CNVs from a BRCA1-mutated patient was accessed from Hill and colleagues dbGap phs001685.v1.p1. Loss of heterozygosity (LOH) score was calculated as described in ref. 28. Mutational signature 3 (Sig 3) analysis was performed using SigMA R package (29) in which counts of different types of single-nucleotide variants (SNV) were used to identify known patterns of mutational processes (30) and to determine the exposure of Sig 3.
Image-based drug-sensitivity testing
Details of the drug-sensitivity testing are shown in Supplementary Materials and Methods. The growth rate–adjusted viabilities were calculated for each drug concentration as described previously (31). The GR50 values were calculated, normalized with respect to the B40 cell line, and visualized using hierarchical clustering heatmap with column normalization. The growth rate over 72 hours was calculated using a formula Gr = LN(N(72 h)/N(0))/72 h.
All statistical analyses were performed by comparing a minimum of three independent measurements. The continuous variables were evaluated for normal distribution and analyzed with nonparametric tests upon non-normal distribution. Groups were compared using Student t test or Mann–Whitney U test. Categorical variables were compared using Fisher exact test. Linear regression was performed using Pearson or Spearman correlation. Multiple comparisons were analyzed using Dunett or Sidak multiple comparison test.
TP53- and BRCA1-deficient cells acquire resistance to multiple PARPis and are cross-resistant to cisplatin
To generate a model system to understand PARPi resistance mechanisms in BRCA1-deficient cells, and as immortalized human fallopian tube epithelial cells are not readily available for extensive genetic perturbations, we used the CRISPR/Cas9 system to delete the TP53 and BRCA1 genes in the retinal pigment epithelial (RPE) cell line (17), thus generating the RPETP53−/−BRCA1−/− cell line, hereafter referred as B40, which showed in vitro hypersensitivity to PARPi (Supplementary Fig. S1C and D).
The B40 cell line was cyclically exposed to increasing concentrations of niraparib or olaparib, and subjected along with six niraparib-resistant cell clones and one olaparib-resistant cell clone to functional and genomic profiling (Fig. 1A). The selected cellular clones were resistant to multiple PARPi, including niraparib (Fig. 1B), olaparib (Fig. 1C), and talazoparib (Fig. 1D). Long-term clonogenic assay confirmed resistance to niraparib (Fig. 1E and F). Furthermore, 7-day survival assays demonstrated that most of the clones were also cross-resistant to cisplatin, except for two clones NA4 and NB1 (Supplementary Fig. S1E and F).
Heterogenous mechanisms of resistance emerge from TP53- and BRCA1-deficient cells exposed to niraparib
Using CRISPR/Cas9, we introduced a deleterious mutation in BRCA1 with the potential of reversion; therefore, we first excluded restoration of BRCA1 protein expression as a mechanism of resistance in the PARPi-resistant clones (Supplementary Fig. S2A and S2B). Next, we tested the cells for (i) RAD51 foci formation, as an intermediate readout for HR-mediated DNA repair (32) and (ii) DNA replication fork stabilization (reviewed in ref. 33). As expected, the B40 cell line lacked RAD51 foci formation both after irradiation (Fig. 2A) and after niraparib treatment (Fig. 2B). In contrast, most of the PARPi-resistant clones, except for one (NB1), had restored RAD51 foci formation after irradiation (Fig. 2A; Supplementary Fig. S2C). We next investigated the sensitivity of the cells to hydroxyurea–induced S-phase arrest and DNA replication fork destabilization as a mechanism of resistance. Interestingly, all of the PARPi-resistant clones exhibited stabilized DNA replication forks when compared with the B40 cell line as quantified using the DNA fiber assay (Fig. 2C).
We next tested the cells for the presence of the other previously described mechanisms of PARPi resistance. First, we profiled the expression levels of NHEJ factors known to suppress HR. Decreased 53BP1 protein levels were observed in NA2, NA3, and NA5 clones (Fig. 2D, top), consistent with reduced number of 53BP1 foci (Fig. 2E; Supplementary Fig. S2D). Furthermore, we observed a decreased REV7 protein level in clone NA1 (Fig. 2D, middle) consistent with reduced REV7/MAD2L2 gene expression levels (Fig. 2F). Because both 53BP1 and REV7 are mediators of NHEJ, their downregulation is expected to improve HR via promoting RAD51 foci formation (13, 14), thus providing a mechanism of PARPi resistance in these clones.
The NB1 clone, which had not restored RAD51 foci, exhibited increased levels of phosphorylated Chk1 protein (pChk1; Fig. 2G) and increased pCHK1 foci (Fig. 2H and I), a kinase that protects the replication fork and promotes PARPi resistance (18). This clone also had lower levels of pRPA foci, compared with B40, suggesting decreased levels of replication stress (Supplementary Fig. S3F). We next determined whether decreased PARP1 protein could account for the acquired PARPi resistance in the clones. We observed increased baseline levels of PARP1 in the B40 cells, compared with RPETP53−/− cells, and reduced expression in clone NA4 (Fig. 2J), a finding that was confirmed by IF (Fig. 2K), indicating NA4 could avoid PARPi action by downregulating PARP1 enzyme.
Consistently, an IF-based PARylation assay, which measures H2O2 stimulation of PAR chains (34), verified significantly reduced PARylation levels in clone NA4 in comparison with the B40 cells (Fig. 2L), suggesting that downregulation of PARP1, and avoidance of PARP trapping, may be the mechanism of resistance of this clone. In addition, clones NA5, OA5, and NAPool, had significantly reduced PARylation levels after H2O2, indicating that avoidance of PARP trapping could enhance PARPi resistance in these clones also (35). Niraparib effectively abolished PARylation in all clones, thus ruling out an efflux pump mechanism of PARPi resistance (Supplementary Fig. S2E). Consistently, the B40 cells showed enhanced PARylation after exposure to H2O2, in comparison to the RPETP53−/− (Fig. 2J and K), in line with the known dependency that HR deficient cells have on PARP1-mediated DNA repair (36).
Taken together, our cellular model recapitulates several of the known mechanisms described for PARPi resistance. Importantly, our results indicate that diverse resistance mechanisms can emerge from a BRCA1-deficient sensitive cell line, and that single clones, such as NA5, can simultaneously exhibit several resistance mechanisms.
PARPi-resistant clones have unique gene expression profiles and are characterized by increased epithelial-to-mesenchymal transition
The PARPi-resistant clones exhibited noticeable heterogeneity on resistance mechanisms; therefore, we hypothesized that they also differ in their gene expression profiles and performed RNA sequencing comparing the clones (Supplementary Fig. S1F). Hierarchical clustering showed that the RPETP53−/− and the B40 cells had the most similar gene expression profiles (Fig. 3A); however, after exposure to PARPi, the gene expression profiles of the niraparib-resistant clones formed unique clusters. Of note, the olaparib resistant clone (OA5) clustered separately from the niraparib-resistant clones, suggesting that distinctive gene expression profiles can emerge from cells exposed to compounds with similar mechanisms of action (Fig. 3A). A MDS plot of the total transcriptomes revealed that the most pronounced differences were among the RPETP53−/−, B40 and OA5 clones in the first two dimensions (Fig. 3B, left). Furthermore, distinct transcriptomic profiles for the NA1, NA3, and NA4 clones were observed in the third and fourth dimensions (Fig. 3B, right).
Analysis of DEGs and GSEA (Supplementary Table S2) showed that most of the clones had unique DEGs in comparison with the resistant pool, thus highlighting the transcriptional diversification across the resistant clones (Supplementary Fig. S3A). GSEA showed that the B40 cells had a significantly higher enrichment score for epithelial-to-mesenchymal transition (EMT) compared with the RPETP53−/− cell line (Fig. 3C). Furthermore, the EMT pathway was significantly enriched in five of the seven PARPi-resistant clones (Fig. 3C). This EMT phenotype was confirmed morphologically (Supplementary Fig. S3B) and by IF for the canonical EMT markers Vimentin (Supplementary Fig. S3C and S3D) and ZEB1 (Supplementary Fig. S3E).
Overall, the clones showed more downregulated than upregulated gene sets (Fig. 3D), indicating that gene downregulation may be a more common mechanism of acquired PARPi resistance. Common findings included downregulation of the mTORC, MYC, and TNFα pathways, which were also downregulated in the B40 cells compared with the RPETP53−/−. Interestingly, the NA5 clone exhibited a significant upregulation of HR and chromatin regulatory pathways (Supplementary Table S2; Fig. 3E), suggesting that restoration of HR may result from changes in chromatin regulation and gene expression. On the other hand, the NB1 clone exhibited downregulation of E2F targets and G2–M cell-cycle pathway genes (Fig. 3F), indicating unique resistance mechanisms potentially related to cell-cycle regulation. In summary, although EMT was common among all PARPi-resistant clones, the individual clones exhibited unique gene expression programs, mainly resulting from downregulation of gene expression.
PARPi resistance correlates with a lower mutational burden and an increased level of genomic instability
To reveal the mutational profiles and patterns of genomic CNVs of the PARPi-resistant clones, we performed WES (Supplementary Fig. S1D). WES confirmed lack of BRCA1 reversion mutation in the resistant clones. Mutation analysis and clustering showed that each of the PARPi-resistant clones had unique mutational clusters (Fig. 4A). In addition, analysis of three replicates from the same culture plate showed higher spontaneous heterogeneity of mutational clusters inside the B40 cell line, when compared with the PARPi-resistant NAPool. Consistently, the resistant clones had, on average, fewer mutations than the B40 cell line, indicating that PARPi resistance is not associated with increased mutational events (Fig. 4B). We next quantified the mutational Sig3 in the B40 and in the PARPi-resistant clones, using the SigMA algorithm. An enriched fraction of mutations belonging to Sig3 was evident in the B40 cell line, consistent with its HR deficiency (Fig. 4C). Interestingly, although six of seven PARPi-resistant clones had recovered the capacity for RAD51 foci formation (Fig. 2A and B), most of the resistant clones still had a high fraction for mutational Sig3, and only two clones (NA2 and NA4) had Sig3 fractions lower than the B40 cell line (Fig. 4C).
We next investigated the degree aneuploidy and genomic CNVs in the cell clones. The computationally predicted ploidy showed good concordance with the cytogenetics (Fig. 4D). The RPETP53−/− cells were mostly diploid (2n), whereas the B40 cells showed diploid (2n) and triploid (3n) cells in equal proportions. Interestingly, PARPi-resistant clones were all mostly polyploid (3n, 4n, and >5n), except for NA4, which showed a mostly diploid (2n) karyotype. Consistently, CNV profiles revealed few CNVs in the RPETP53−/− cell line and increased CNVs and allelic imbalances in the B40 cell line and in most of the resistant clones (Supplementary Fig. S4). The NA4 clone showed lower levels of genomic instability in comparison with all other resistant clones, consistent with its reduced levels of PARP1 expression as a distinct mechanism of resistance.
Genomic instability and LOH events have been proposed as biomarkers for PARPi sensitivity (7). Calculation of LOH scores showed, however, that all the resistant clones and the NAPool had significantly more LOH events than the B40 cells (Fig. 4E). Taken together, our genomic analyses indicate that the genomes of these TP53–/− and BRCA1–/− PARPi-resistant clones have unique mutational profiles, lower overall mutational burden, mostly triploid karyotypes, and increased genomic instability in comparison with the PARPi-sensitive cell line. A summary of the functional and genomic findings in the clones are shown in Fig. 4F.
PARPi resistance results from clonal selection in the cellular model and in BRCA1-mutated high-grade serous ovarian cancer
We next investigated the clonal evolutionary patterns of acquired PARPi resistance, using WES data in our cellular model system, and also in tumor samples of a patient with HGSC with BRCA1 germline mutation and acquired PARPi resistance (37).
In the evolutionary tracing of the cellular model, we observed three subclones (clusters no. 1, no. 4, and no. 5), already present in the B40 cell line, indicative of early clonal heterogeneity in BRCA1-deficient cells (Fig. 5A). After exposure to PARPi, the pool of acquired resistance (NAPool) had increased clonal heterogeneity, with five distinct subclones. Remarkably, cluster no. 5, already present in the B40-sensitive cell line, was selected for and enriched in the resistant NAPool. Importantly, cluster no. 5 was predicted to be the origin of six of the seven PARPi-resistant clones via clonal selection and genomic evolution. The time required for clonal evolution differed across the clones, with clones NA2 and NA3 arising earlier, and NA1, NA5, and NB1 arising later (Fig. 5A), consistent with the varying growth rates of the cell lines (Supplementary Fig. S5A). In contrast, the only diploid resistant clone NA4, did not emerge from cluster no. 5, but rather directly from cluster no. 1, which might partially explain its diploid karyotype and distinct mechanism of PARPi resistance (i.e., PARP1 downregulation). Interestingly, a smaller subclone (cluster no. 4) was also present in the B40 cell line; however, this subclone was outselected during PARPi exposure. Interestingly, three of seven PARPi-resistant clones showed lower risk ratios for Sig3 in the mutations accumulated later in pseudotime in the evolutionary model (Supplementary Fig. S5B).
We next studied tumor samples from a patient with BRCA1-deficient HGSC and acquired resistance to PARPi (37), and explored whether clonal evolutionary patterns existed during the acquisition of PARPi resistance. A treatment-naïve sample from the primary ovarian tumor was sequenced from an archival biopsy (Fig. 5B). However, likely due to the sample type (archival formalin-fixed paraffin-embedded–derived DNA) we only detected one genomic clone in this sample (cluster no. 1). At the time of clinical PARPi resistance, fresh tumor biopsies were obtained from four metastatic sites (Fig. 5B and C). Seven unique PARPi-resistant subclones with distinct mutational patterns were identified (Fig. 5D; Supplementary Fig. S5C). Evolutionary modeling of this HGSC predicted the emergence of all seven heterogenous PARPi-resistant subclones from subclone no. 3 that was detected in the rectosigmoid biopsy, and had further spread inside the abdominal cavity to different metastatic sites. Subclones no.6 and no. 7 evolved early from subclone no. 3 and followed distinct evolutionary trajectories; subclone no. 6 remained in the rectosigmoid area without further evolution; subclone no. 7 metastasized into the upper abdomen, giving rise to distinct subclone no. 4 located in the supracolic omentum and subclone no. 5 located on the diaphragm. Furthermore, emerging from subclone no. 3, subclone no. 8 was detected in the transcolonic biopsy, which further evolved into unique subclone no. 2 (Fig. 5D).
Consistently with our cellular model, the clinical PARPi-resistant subclones exhibited lower numbers of mutations (Fig. 5E) and had differentially retained mutational Sig3 (Fig. 5F). Furthermore, we observed heterogeneity in Sig3 among the clinical samples, indicating that HR restoration occurred in some but not all clinical PARPi-resistant clones. Interestingly, subclones no. 2 and no. 6, showed particularly lower risk ratios for Sig3 positivity (Fig. 5F; Supplementary Fig. S5D). Genomic prediction of ploidy also showed a predominantly triploid karyotype in three of four PARPi-resistant tumor samples (Fig. 5G). Interestingly, lower LOH scores were observed in three out of four tumor samples, especially in the R-Sigmoid tumor containing subclone 6 that also had lost Sig3 (Fig. 5H), this subclone may have restored HR and acquired a different mechanism of resistance compared with the other subclones.
Acquired PARPi resistance results from clonal selection of preexisting drug-tolerant cells
Our results suggested that PARPi resistance arose from a single clonal precursor that preexisted in the overall-sensitive BRCA1-deficient cell population. To experimentally validate the preexistence of this clone, we isolated 10 single-cell clones from the B40-sensitive cell line and exposed them to niraparib. In this experiment, we confirmed that cellular subclones with heterogeneous responses to niraparib preexisted in the B40 cell line (Supplementary Fig. S5E). In fact, two of these 10 subclones (B40_C9 and B40_C5) had significantly increased IC50s in comparison with the B40 cell line, whereas subclone (B40_C6) was highly sensitive to niraparib (Fig. 5I). Interestingly, in functional experiments, the B40_C5 subclone showed increased RAD51 foci formation (Supplementary Fig. S5F), and decreased pRPA and γH2AX foci after IR (Supplementary Fig. S5G and S5H) indicative of increased HR capacity and decreased replication stress.
On the basis of WES data, we identified chromosome 21q loss as a genomic marker for tracking the clonal evolution trajectory and predict the preexistence of a drug-tolerant subclone in the B40 cell line. We predicted that 21q loss is a genomic marker for subclone no. 5 and the emerging PARPi-resistant clones. Quantification of this genomic marker via 21q chromosome painting showed a high proportion of 21q loss in the drug tolerant subclone B40_C9, and enrichment of this genomic marker in the fully resistant clone NA3 (Fig. 5J). These results suggest that B40_C9 functionally represents subclone no. 5, detected in the B40 cells that was evolutionally selected and gave rise to all of the resistant clones. Consistently, 21q loss was underrepresented in the PARPi-hypersensitive B40_C6 subclone (Fig. 5J), and in the PARPi-resistant clone NA4 that was not derived from subclone no. 5 (Supplementary Fig. S5I). Cytogenetic analysis confirmed a mostly diploid karyotype in the sensitive B40_C6 subclone, an increasing amount of polyploid cells in the drug-tolerant B40_C9 subclone, and triploid karyotype dominating the fully resistant NA3 clone (Supplementary Fig. S5I). The genomic profiling and evolutionary analyses thus suggested that BRCA1 deficiency gives rise to a chromosomally unstable cell population with intrinsic clonal heterogeneity that can contain preexisting drug tolerant, partially HR-restored cells.
Heterogeneous PARPi-resistant clones have common and unique vulnerabilities
Because of the observed genetic and functional heterogeneity, we next sought rationale approaches for targeting PARPi-resistant clones. On the basis of the transcriptomic analysis, cell-cycle–related programs appeared to be actionable targets. To detect these potential vulnerabilities, we first performed flow cytometry–based cell-cycle profiling at baseline and after niraparib rechallenge (Fig. 6A). At baseline, we observed a significantly increased proportion of cells in S-phase in the B40 cell line, and in three of seven PARPi-resistant clones, as compared with the RPETP53−/−. Furthermore, the NB1 clone, which is deficient in RAD51foci, had a unique cell-cycle profile, with increased proportion of cells in G2 and decreased proportion of cells in G1-phase. Noticeably, upon niraparib rechallenge, the proportion of cells in G2-phase was increased in all the resistant clones, similar to the sensitive B40 cell line (Fig. 6B). In addition, a shorter G1-phase was observed in two clones (NA5 and NB1) after niraparib. These cell-cycle aberrations indicated activation of DNA damage response in the PARPi-resistant cells after niraparib rechallenge, which was investigated by profiling the formation of pRPA, γH2AX, and pCHK1 foci before and after niraparib. Consistent with restored replication fork stability, all clones had decreased levels of baseline nuclear pRPA foci, indicating decreased replication stress (Fig. 6C). However, upon rechallenge with niraparib, all resistant clones still exhibited increased nuclear foci for pRPA (Fig. 6C) and pCHK1 (Supplementary Fig. S6A), indicative of an activated DNA damage response. Consistently, four of seven resistant clones showed decreased DNA double-strand breaks measured as γH2AX foci at baseline (Fig. 6D). However, all clones, except for clone NA4 with decreased PARP1, exhibited increased γH2AX foci after PARPi rechallenge (Fig. 6D). We next quantified the number of chromosomal aberrations in metaphase spreads from the clones after niraparib treatment, interestingly, all clones still showed increased numbers of chromosomal aberrations (Fig. 6E), indicating that homologous recombination was not fully restored even in the clones with RAD51 foci formation.
We next selected targeted therapeutics based on potentially actionable resistance mechanisms and gene expression programs of the PARPi-resistant clones (Supplementary Table S2). We performed a systematic drug-sensitivity test using an automatized platform with an imaging-based readout that adjusts for the differential growth rate of the various clones (Fig. 6F; ref. 38). We tested targeted agents and correlated the sensitivity of PARPi-resistant clones to drug-induced alterations in cell-cycle profiles. First, as the B40 cells and the PARPi-resistant clones had different growth kinetics compared with the RPETP53−/− cells (Supplementary Fig. S5A), we confirmed the resistance to niraparib using the growth-normalized assay (Supplementary Fig. S6B). Second, in the drug response profiles, resistant clones with downregulation of NHEJ (NA1, NA2, and NA3) had similar profiles of sensitivity to prexasertib (Chk1/2i), adavosertib (WEE1i), and berzosertib (ATRi; Fig. 6G). Interestingly, NA1 and NA2 were more sensitive to abemaciclib (CDK4/6i), when compared with B40. Similarly, NA4 was sensitive to Chk1/2i, CDK4/6i, and WEE1i, but resistant to ATRi, when compared with the B40 cells. In line with the observed chromatin regulatory pathway rewiring in the gene expression profile, NA5 was the most sensitive clones to belinostat (HDACi), but showed resistant to CDK4/6i and WEE1i. NB1, the only clone without RAD51 restoration, showed lower sensitivity to all of the agents compared with the B40 cell line, and was resistant to the ATRi with an almost 2-fold IC50 compared with the RPETP53−/− cells. OA5, the Olaparib-resistant clone, was sensitive to CDK4/6i and ATRi, but had low sensitivities to Chk1/2i, WEE1i, and HDACi.
CDK4/6i induced a robust G1 arrest in the B40 cells and in five out of six PARPi-resistant clones (Fig. 6H). G1 arrest was not observed in the CDK4/6i-resistant NA5 clone. Furthermore, CDK4/6i induced a decrease in the proportion of cells in S-phase in all clones, except the RPETP53−/−. Interestingly, responses to ATRi significantly correlated with the drug-induced cell-cycle aberrations; the PARPi-resistant clones that responded (n = 4/7) to ATRi, showed a significantly decreased proportions of cells in S-phase and increased proportions of cells in G2 (Fig. 6I). Similarly, PARPi-resistant clones that were sensitive to Chk1/2i Prexacertib (n = 4/7) showed a significant decrease of cells in S-phase, but there was no significant correlation to increase in G2 (Fig. 6J). This correlation of responses and cell-cycle profiles is consistent with the mechanisms of action of ATRi and Chk1/2i in disrupting the DNA damage response, and indicates dependency of these clones to that checkpoint (18). HDACi and WEE1i did not cause cell-cycle aberrations that were significantly associated with drug sensitivities. In summary, the PARPi-resistant clones showed mechanism-dependent vulnerabilities to the selected agents, that for certain drugs associated with cell-cycle aberrations. However the heterogeneity of the PARPi-resistant clones led to heterogeneous responses to our targeted screening and shows that clones could not be targeted with a single drug.
As PARPis are entering wide clinical use, acquired PARPi resistance is an emerging clinical problem in the treatment of multiple cancer types, including HGSC. We engineered BRCA1-deficient cells that are resistant to PARPi and showed that clones derived from these cells, via evolutionary selection, have significant functional and genomic heterogeneity. Similar genomic features and evolutionary patterns were observed in a clinical BRCA1-mutated tumor with acquired PARPi resistance. Importantly, we show that these heterogenous PARPi-resistant clones are vulnerable to mechanism-specific targeting.
The best-documented mechanism of resistance to PARPi or platinum agents in human tumors involves the restoration of HR via somatic reversion of the original mutation conferring HR deficiency. Multiple reports have confirmed the relevance of these somatic reversions in therapy-resistant ovarian cancers (10, 11, 22). However, the prevalence of secondary mutations restoring BRCA1/2 function has varied from 0% to 21% (39, 40) in ovarian cancer to as high as 40%–50% in breast cancer (40, 41). In our study, we did not observe BRCA1 reversion mutations in the PARPi-resistant clones or in biopsy samples from a patient with BRCA1− germline HGSC with acquired PARPi resistance. Accordingly, PARPi resistance mechanisms other than reversion mutations are likely to exist also in the clinical setting.
Genomic cancer studies support a model in which drug resistance is driven by evolutionary selection of a preexisting clone rather than acquisition of new mutational events (refs. 42–45 and reviewed in ref. 46). Consistently, we show that a PARPi-tolerant subclone preexisted within a heterogenous BRCA1-deficient cell population and was clonally selected during the evolutionary pressure imposed by the PARPi treatment. In our study, PARPi-resistant clones displayed increased chromosomal instability, modifying the cellular karyotype, the cellular system of inheritance, affecting the topology of the genome and the transcriptomes (47). Here, we propose that the chromosomal instability in the BRCA1-deficient cells, amplified by the effects of PARPi on further disrupting genome integrity (48), provides an avenue for improved fitness, increased clonal heterogeneity, and selection of preexisting drug-resistant cells leading to PARPi resistance. As HGSC is among the tumors with highest levels of clonal heterogeneity (49), the existence of intrinsically PARPi-resistant cells at tumor presentation is likely also in the clinical setting.
Previous studies on PARPi resistance have been performed in commercially available BRCA1-deficient cell lines derived from drug-resistant tumors (20, 21) and have focused on single mechanism of resistance (13, 14). In contrast, our genetically engineered model system suggested that acquired PARPi resistance involves the adoption of several distinct, potentially simultaneous resistance mechanisms. Consistently, the PARPi-resistant clones showed molecular heterogeneity with unique transcriptomic and mutational profiles. This heterogeneity was recapitulated at the clonal level in a BRCA1-mutated tumor with acquired PARPi resistance, further supporting the roles of clonal and functional heterogeneity as molecular underpinnings of acquired PARPi resistance.
Measurements of genomic instability, such as LOH and mutational Sig3 resulting from error-prone DNA repair processes, have been suggested as biomarkers for PARPi sensitivity (50). Regardless of the fact that the majority of the PARPi-resistant clones had restored RAD51foci formation, most of the resistant clones still retained mutational Sig3, DNA damage, and chromosomal aberrations after retreatment with niraparib. In line with previous observations from cell lines and mouse models (13), these findings suggest that restoration of HR in the clones via the mechanisms described herein is only partial.
Despite their highly heterogeneous nature, the PARPi-resistant clones shared vulnerabilities to targeted agents. Most of the clones exhibited cell-cycle abnormalities upon reexposure to niraparib, indicating residual vulnerability to PARPis. However, the clones showed heterogenous responses for targeted chemotherapeutic agents, and a single drug was not able to efficiently target all PARPi-resistant clones. Some clones (NA1, NA2, and NA3) with a resistance mechanism involving suppression of the NHEJ pathway had a similar drug-sensitivity profile, whereas other clones (NA4, NA5, and NB1) with distinct mechanisms of resistance were overall less drug sensitive. NA4 and NB1, with distinct mechanisms were the only two clones with persistent cisplatin sensitivity. Taken together, the PARPi-resistant clones exhibit mechanism-dependent vulnerabilities that can potentially be exploited in combination therapies. The optimal way of targeting the heterogenous PARPi-resistant clones in combinations or sequentially, should be addressed in future in vitro and in vivo experiments or via for example, mathematical modeling.
We acknowledge certain limitations of this study. First, we used an epithelial cell line not originating from fallopian tube and generated a deleterious BRCA1 mutation not easily reversible to explore some of the previously described mechanisms for PARPi resistance. Thus, it is possible that additional mechanisms for PARPi resistance, including reversion mutations could exist in the clones if different approaches were taken. Second, we only had access to sequencing data from one patient with HGSC with acquired PARPi resistance. In this sample set, the original PARPi-sensitive ovarian tumor was sequenced from formalin-fixed tissue, compared with the PARPi-resistant samples, which were analyzed from fresh frozen tissue. We did not observe multiple clones in the PARPi-sensitive sample, potentially due to the lower sequencing quality and resolution in variant calling (50). Moreover, we did not have access to additional fresh clinical patient samples, which could be analyzed with similar functional studies as in the cellular model. Therefore, we were not able to verify the preexisting resistant subclones in the PARPi-sensitive patient sample, that were in fact verified to preexist in the cellular model. Therefore, future studies should focus on fresh tumor biopsies taken before and after PARPi treatment. These studies will be critical for revealing the mechanisms, development of biomarkers, and clinical targeting of HGSC with acquired PARPi resistance.
No disclosures were reported.
A. Färkkilä: Conceptualization, formal analysis, investigation, visualization, methodology, writing–original draft, writing–review and editing. A. Rodríguez: Conceptualization, formal analysis, writing–review and editing. J. Oikkonen: Formal analysis, visualization, methodology, writing–review and editing. D.C. Gulhan: Formal analysis, methodology, writing–review and editing. H. Nguyen: Formal analysis, writing–review and editing. J. Domínguez: Formal analysis, writing–review and editing. S. Ramos: Formal analysis, writing–review and editing. C.E. Mills: Formal analysis, methodology, writing–review and editing. F. Pérez-Villatoro: Formal analysis, visualization, writing–review and editing. J.-B. Lazaro: Formal analysis, methodology, writing–review and editing. J. Zhou: Formal analysis, writing–review and editing. C.S. Clairmont: Methodology. L.A. Moreau: Formal analysis. P.J. Park: Methodology. P.K. Sorger: Resources, methodology. S. Hautaniemi: Resources, supervision, writing–review and editing. S. Frias: Resources, supervision, writing–review and editing. A.D. D'Andrea: Conceptualization, resources, supervision, writing–review and editing.
This work was supported by OCRA, Sigrid Jusélius Foundation, and Biomedicum Helsinki Foundation to A. Färkkilä, and NIH Grant U54-HL127365 to P.K. Sorger. The work was also supported by the Breast Cancer Research Fund (BCRF), the Richard and Susan Smith Family Foundation, The HMS/MIT Bridge Project, and NIH Grant P50CA240243 to A. D'Andrea, and in part by the European Union's Horizon 2020 research and innovation programme under Grant Agreement No. 667403 for HERCULES to J. Oikkonen. We wish to thank André Madeira for comments.
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