Introduction

Tyrosine kinases have emerged as important drug targets in cancer therapy due to their druggability and pivotal roles in cell proliferation and survival(1). They are implicated in various aspects of cancer development(2), such as cell survival, proliferation, angiogenesis, and invasion, making them attractive targets for drug intervention. Consequently, tyrosine kinase inhibitors (TKIs) have gained considerable attention as primary agents for cancer treatment.

Triple negative breast cancer (TNBC) treatment has limited options for targeted therapy. TNBC, characterized by the absence of estrogen receptor, progesterone receptor, and HER2 expression, exhibits elevated activity of tyrosine kinases, including EGFR and IGF1R(3, 4). However, several clinical trials investigating TKIs, such as VEGFR inhibitors, EGFR inhibitors, and FGFR inhibitors, in TNBC treatment have yielded disappointing results due to inadequate efficacy. Therefore, it is crucial to comprehend the mechanisms underlying TNBC’s suboptimal response to TKIs to enable the development of more effective targeted therapies against TNBC.

The therapeutic efficacy of TKIs is compromised by intrinsic and acquired resistance(5). For instance, EGFR inhibitor gefitinib extended the median progression-free survival by only five months compared to conventional chemotherapy in non-small cell lung cancer (NSCLC) patients with EGFR mutation(6). Significant subset of drug resistance is driven by gene interactions that enable compensatory changes in signal transduction upon drug treatment. Compensatory activation of mitogenic signals, such as MET, PIK3CA amplification, and MAPK/ERK signaling activation, counterbalances the inhibition of EGFR by TKI osimertinib in a significant portion of NSCLC patients(7). Simultaneous inhibition of multiple signaling molecules that compensate for each other’s loss is proposed as an effective strategy to overcome resistance to kinase inhibitor therapy, emphasizing the importance of combinatorial therapy(8).

Until recently, a highly scalable method for screening combinatorial therapy has been lacking. Combinatorial CRISPR screens have emerged as efficient tools to identify synergistic targets for combinatorial therapy. We and others recently developed combinatorial CRISPR screens to elucidate pairwise gene interactions(9–12). Our combinatorial genetic screen platform, combinatorial genetics en Masse (combiGEM), was successfully implemented to identify combinations of epigenetic regulators causing synthetic lethality in ovarian cancer cells (9).

In this study, we utilize CombiGEM-CRISPR technology to identify tyrosine kinase inhibitor combinations with synergistic effect in TNBC cell line and xenograft models for potential combinatorial therapy against TNBC. We highlight FYN as a key therapeutic target that, when inhibited, enhances the cytotoxic effect of inhibition of other tyrosine kinases (IGF1R, EGFR, and ABL2). Mechanistic studies reveal KDM4 as a crucial epigenetic regulator that demethylates H3K9me3 and transcriptionally upregulates FYN upon TKI treatment. In vitro and in vivo validation demonstrates the synergistic TNBC-shrinking effects of combining PP2, saracatinib (Src family kinase / FYN inhibitor) or QC6352 (KDM4 inhibitor) with TKIs. Additionally, we demonstrate the clinical significance of our findings by observing upregulation of FYN in various models of drug tolerant persisters and residual tumors after chemo-, radio-, or targeted therapy. Therefore, simultaneous targeting of FYN-KDM4 and tyrosine kinase pathways through combinatorial therapy holds promise for effective therapy against TNBC.

Results

Pairwise tyrosine kinase knockout CRISPR screen reveals synergistic tyrosine kinase inhibition combination

For efficient translation of CRISPR screening data to drug combination, we selected 76 tyrosine kinases that could be inhibited by at least one drug from the drug repurposing hub database (table S1) (13). For pairwise CombiGEM library construction, we chose three guide RNAs from the optimized Brunello single guide RNA (sgRNA) list(14) employing the iterative cloning method as previously described(9, 15). The resulting library enabled screens of pairwise knockouts of the 76 tyrosine kinase genes, encompassing 54,289 sgRNA pairs representing 3,003 pairwise gene disruptions (Fig. 1A). To validate our library, we performed next-generation sequencing and confirmed that 99.5% (2,989/3,003) of gene pairs were represented by at least 6 pairs of sgRNAs, with the log_{10 r}eads per million of 0.5 (Fig. S1A).

Figure 1.

Triple negative breast cancer cell line MDA-MB-231 cells stably expressing Cas9 were transduced with the lentiviral library at low multiplicity of infection (MOI) of 0.3. Genomic DNA was harvested 3 days after transduction (designated as day 0[D0]), and 23 days after transduction (D20) (Fig. 1A) to perform PCR amplification of sgRNA pairs for subsequent next-generation sequencing (NGS) analysis. sgRNAs, instead of barcodes in our previous CombiGEM screens, were directly sequenced using paired-end sequencing to rule out uncoupling of barcodes of sgRNAs and barcodes (Fig. S1B-C). We counted the occurrences of each sgRNA pair in the NGS data and calculated the normalized log2 fold change in counts between the day 20 and day 0 samples as the growth phenotype score Z (see Methods). The Z scores for the two permutations of an sgRNA pair (r=0.50 between sgRNA-A + sgRNA-B and sgRNA-B + sgRNA-A pairs), the biological replicates, (r = 0.74 between replicates #2 and #3), and independent sgRNA pairs targeting the same set of genes were positively correlated (r=0.3-0.72) (Fig. S1D-G).

Gene pairs that synergistically kill cells were identified by calculating gene interaction scores (GI). The GI scores were derived by comparing the growth phenotype score Z resulting from the disruption of a gene pair (Z_A+B, observed Z score) to the sum of Z scores obtained from the disruption of each gene individually within the pair (Z_{A+Con +} Z_B+Con, expected Z score) (Fig. 1B). The expected and observed Z scores for each gene (or sgRNA) pair exhibited a strong positive correlation (r=0.97 gene level, r=0.88 sgRNA level), suggesting that most random pairwise combinations of tyrosine kinase perturbations show additive effects (Fig. 1B and S1H). GI scores were calculated by quantifying each gene pair’s normalized deviation from the quadratic fit of the expected-observed Z score plot(10) (Fig.1C and Fig. S1I, see methods).

We selected thirty synthetic lethal gene pairs using cutoffs for gene level GI score <-2 and p<0.01 for GI scores determined by RIGER analysis(16) and Z<-5 (Fig. 1D and dataset S1). Among these, the SRC-YES pair was one of the strongest synthetic lethal gene pairings (GI= −3.95). Notably, SRC-YES belong to the same tyrosine kinase family and are known to be functionally redundant and are expected to be synthetic lethal(17). These findings provide evidence for the effectiveness of our screening approach in identifying synthetic lethal gene pairs.

Synthetic lethal gene pairs are next validated by expressing the pair of sgRNAs targeting them. To achieve this, we introduced lentiviral vectors carrying two distinct sgRNAs targeting the candidate synthetic lethal pairs, each tagged with a different fluorescent protein (GFP and mCherry). MDA-MB-231 Cas9 cells were transduced with the lentivirus at a low titer (MOI ∼0.5), resulting in a mixed population of cells expressing either one or both sgRNAs along with their respective fluorescent proteins (Figs. 2A and S2A). We monitored the decrease in the number of GFP/mCherry double-positive cells to evludate synthetic lethality (see methods). We validated the efficacy of the sgRNAs used in Figure 2A through the T7 endonuclease assay, which confirmed efficient gene editing (Fig. S2B). Consistent with our CRISPR screening results, we observed that the disruption of six out of eight synergistic target gene combinations led to a reduction in cell viability beyond what was predicted by the Bliss independence model (Fig. 2B). Moreover, the relative viability of double knockout cells and the rate of synergistic killing demonstrated a strong correlation with our screening data (r = 0.65 for both viability and synergistic effect; Fig. S2C). Collectively, our findings provide compelling evidence that our screening approach successfully identified synthetic lethal gene pairs with a high level of confidence.

Figure 2.

FYN inhibition synergizes with IGF1R, EGFR, ABL2 inhibitions in cell killing

We noticed that several validated synergistic target gene pairs included FYN (e.g. FYN+IGF1R, FYN+EGFR, and FYN+ABL2). Notably, network analysis of the 30 candidate synergistic tyrosine kinase pairs revealed that FYN is one of the key nodes participating in synergistic interactions with multiple genes (Fig. 2C). Expression of FYN, a member of Src family kinase, has been implicated in cancer malignancy including drug resistance(18–21). Particularly, recent studies highlighted significant contribution of FYN in TNBC malignancy by promoting epithelial-to-mesenchymal transition (EMT)(22, 23). Interestingly, we found that FYN, but not SRC, exhibited significant upregulation in triple-negative breast cancer (TNBC) compared to other subtypes, as evidenced by microarray data from primary tumor samples (24) and the cancer cell line encyclopedia (CCLE)(25) (Figs. 2D-E). In contrast, other key nodes in figure 2C, including FGFR2, FRK and TEK were not expressed at appreciable levels in MDA-MB-231 (log2(TPM+1) for TEK: 0.0704, FRK:0.124, FGFR2:0.227), and their expressions were not significantly upregulated in TNBCs compared to other breast cancer subtypes (Fig. S3). Therefore, we proceeded further in focusing on validating FYN as key candidate synthetic lethal gene. These findings suggest that FYN could represent an attractive drug target for TNBC treatment. To investigate this further, we assessed whether simultaneous inhibition of FYN by PP2, which selectively targets the SRC family kinase inhibitor with the highest potency against FYN, in combination with other kinase inhibitors (TKIs), could inhibit cancer cell growth(26). PP2 as a single agent significantly downregulated MDA-MB-231 cell viability (Fig. S4A). Therefore, we focused on synergistic cell death by TKI combinations above additive effects by each TKI. To this end, analysis using SynergyFinder plus(27)revealed that all TKI combinations involving PP2 and NVP-ADW742 (IGF1R inhibitor), gefitinib (EGFR inhibitor) or imatinib (ABL inhibitor) synergistically induced cell death in MDA-MB-231 cells (Fig. 2F). Dose-response curves demonstrated that co-treatment with PP2 reduced the IC50 of the tested TKIs by 34-61%, indicating that PP2 sensitized cancer cells to TKI treatment (Fig. 2G). Similar synergy was observed when TKI combinations included saracatinib, a SRC family kinase inhibitor(28), in place of PP2 (Fig. S4B). Moreover, specific ablation of FYN, but not SRC, sensitized cells to TKIs, highlighting the critical role of FYN as a member of SRC kinase family responsible for TKI resistance (Figs. 2H and S4C). Ablation of FYN itself did not significantly decrease cell viability (Fig. S4D). Importantly, we observed similar synergy between the same drug combinations in other TNBC cell lines, including Hs578T, HCC1143, HCC1395, and HCC1937 cells (Figs. S4E-H). Further assessment using live-dead and BrdU assays revealed that both the PP2+NVP-ADW742 and PP2+gefitinib combinations synergistically induced cell death while inhibiting cell growth (Fig. 2I).

Persistent activation of MAPK pathway and PI3K-AKT pathway has been associated with TKI resistance in various cancers(5). Therefore, we investigated which downstream pathways were involved in sensitizing cells to TKI treatment. Notably, the p38 MAPK was significantly attenuated following treatment with either PP2 or saracatinib treatment (Fig. 2J). Previous studies with imatinib resistant CML cells identified ERK signaling as critical downstream of FYN activation(19, 20). However, FYN inhibition failed to significantly downregulate phosphorylated ERK level upon imatinib treatment, indicating downstream signals of FYN leading to drug resistance may be context dependent. Genetic ablation of FYN similarly reduced p38 activation (Fig. 2K). Attenuation of p38 activity was also observed in an independent TNBC cell line, Hs578T (Figs. S4I-J). Importantly, treatment of p38 MAPK pathway inhibitor SB203580 markedly sensitized cells to TKI treatment (Fig. 2L), while SB203580 as single agent did not significantly change cell viability (Fig. S4K)

FYN mRNA is induced upon TKI treatment in KDM4 dependent manner

Our discovery that inhibition of FYN synergizes with multiple TKIs possessing distinct target profiles suggests that FYN may play a role in general resistance mechanisms against TKI therapy. Consistently, we observed an increase in both protein and mRNA levels of FYN following TKI treatment, indicating that upregulation of FYN confers compensatory survival signal in TKI-treated cells (Fig. 3A-B). The phosphorylation levels of FYN was increased proportional to FYN protein level, indicating specific kinase activity of FYN did not change (Fig. S5A). Previous study suggested that increased expression of EGR1 transcription factor is responsible for FYN mRNA accumulation in imatinib resistant CML(18). Consistently, EGR1 expression was increased upon TKI treatment in MDA-MB-231 cells. However, EGR1 expression was not increased in TKI treated MDA-MB-231 cells, nor did its knockout significantly downregulated FYN mRNA levels (Fig. S5B). To elucidate the mechanisms underlying the accumulation of FYN, we treated MDA-MB-231 cells with inhibitors targeting key epigenetic modifiers and assessed their synergistic effects with NVP-ADW742 in cell killing, as well as their impact on FYN mRNA accumulation. Multiple drugs, including pinometostat (DOT1L inhibitor(29)), tazemetostat (EZH2 inhibitor(30)), A366 (G9a inhibitor(31)) and GSK-J4 (KDM6 inhibitor(32)) strongly decreased cell viability upon TKI treatment (Fig. S5C). As the increase in FYN mRNA is responsible for TKI resistance, we reasoned that the drug that directly affect FYN mRNA level and hence cell viability should be an inhibitor of epigenetic regulator that enhances transcription. To this end, we focused on pinometostat and GSK-J4 for further validations. Intriguingly, while GSK-J4 decreased FYN mRNA upon NVP-ADW742 treatment, pinometostat failed to decrease it (Fig. S5D).. Consistent with this, treatment with NVP-ADW742 increased the expression of most members of the jumonji domain histone demethylase family (Fig. 3C). This observation is consistent with a previous study on taxane-resistant H1299 lung cancer cells(33), suggesting that histone demethylases may play critical roles in activating a drug resistance gene program. However, the ablation of KDM6, the primary targets of GSK-J4, failed to significantly decrease FYN expression (Fig. S5E). GSK-J4 is known to inhibit other jumonji domain histone demethylase family proteins including KDM4 and KDM5(34). Therefore, we tested the possibility that other histone demethylase may be involved in regulating FYN expression. Among jumonji domain histone demethylases, KDM4, and to a lesser extent KDM3, were the only gene family members whose ablation inhibited FYN upregulation and p38 activation upon TKI treatment (Figs. 3D and S5F). Ablation of KDM5, which has been shown to induce drug tolerance in cancer cells(35), did not significantly alter FYN expression (Fig. S5G). Like NVP-ADW742 treatment, gefitinib treatment increased KDM4 demethylase levels (Fig. S5H). We also analyzed two independent TNBC organoids obtained from primary tumors and found concurrent upregulation of KDM4 with FYN mRNAs upon NVP-ADW742 and gefitinib treatment (Fig. S5I). Critically, time course experiment with NVP-ADW742 treated MDA-MB-231 revealed that accumulation of KDM4A protein preceded FYN protein (Fig. 3E), suggesting that KDM4A accumulation may be responsible for FYN accumulation. Both KDM3 and KDM4 demethylates methylated H3K9, thereby promoting the opening heterochromatin for transcription(36). Remarkably, expression of KDM4A, the most abundantly expressed gene among KDM4 demethylases in TNBC cell lines (Fig. S6A) was enriched in TNBC compared to other breast cancer subtypes (Fig. 3F) and was positively correlated with FYN expression in CCLE database, suggesting that KDM4 regulates FYN mRNA levels (Fig. 3G). Genetic ablation of KDM3 or KDM4 (Fig. S6B-C) decreased FYN and p38 activity. Also, genetic ablation of KDM3 or KDM4 sensitizing MDA-MB-231 cells to TKIs (Figs. 3H-I). The Ablation of KDM3 or KDM4 only had modest but statistically insignificant effect on cell viability (Fig. S6D). Likewise, treatment of KDM4 inhibitor QC6352(37) synergized with TKIs in killing MDA-MB-231 cells (Fig. 3J). QC6352 treatment also significantly attenuated FYN accumulation upon NVP-ADW742 treatment (Fig. 3K-L). This was consistent with the RNA sequencing data results in the previous study with breast cancer stem cells treated with QC6352(38). Specifically, FYN was the most significantly downregulated SRC family kinase upon QC6352 treatment (Fig. 3M). Analysis of chromatin IP (ChIP) sequencing data from the same study revealed KDM4A enrichment near FYN promoter; and QC6352 treatment increased H3K9me3 enrichment at the same locus (Fig. 3N). Indeed, this FYN promoter locus exhibited a reduction in H3K9me3 following NVP-ADW742 treatment, while QC6352 treatment restored H3K9me3 enrichment (Fig. 3O). This finding suggests that KDM4 may directly demethylate H3K9me3 at FYN promoter to upregulate FYN transcription. In contrast, H3K27me3 marks, which is demethylated by KDM6 family demethylases, were not significantly changed at FYN promoter upon NVP-ADW742 treatment (Fig. S6E). FYN accumulation and resistance to TKIs were also confirmed to be attenuated by QC6352 treatment in other independent TNBC cell lines (Figs. S6F-G).

Figure 3.

FYN/KDM4 inhibition synergizes with TKI treatment in vivo

We proceeded to investigate the potential clinical application of our synthetic lethal gene pairs as combinatorial therapy by assessing the in vivo efficacy of pharmacological interventions targeting these gene pairs using MDA-MB-231 xenograft models. Strikingly, co-treatment of saracatinib and NVP-ADW742 synergistically reduced tumor size, whereas treatment with either agent alone was ineffective in slowing tumor growth (Fig. 4A). All treatment groups exhibited minimal changes in body weight, indicating that the overall health of the animals was not adversely affected by the combination treatment (Fig. S7A). Saracatinib-gefitinib combination was not tested as saracatinib can inhibit EGFR(28). Similarly, KDM4 inhibitor QC6352 synergized with gefitinib in reducing MDA-MB-231 xenograft tumor growth without causing overt changes in animal health (Figs. 4B and S7B). Additionally, the expression levels of FYN and KDM4A were found to be correlated with poor prognosis in a previously reported breast cancer cohort(24), highlighting the potential of targeting these two genes as therapeutic targets for TNBC (Fig. 4C). Collectively, our results demonstrate that upregulation of KDM4 upon TKI treatment reduces H3K9me3 mark in FYN enhancer, thereby increasing FYN expression and promoting cell survival under TKI treatment (Fig. 4D).

Figure 4.

FYN is associated with drug tolerant persister phenotype

The observed epigenetic alterations in regulators conferring resistance to multiple cancer drugs closely resemble non-genetic changes associated with the generation of drug-tolerant persisters(35). Indeed, prolonged incubation of MDA-MB-231 cells treated with TKIs or conventional chemotherapy drugs such as doxorubicin or paclitaxel resulted in increased levels of FYN (Fig. 5A). Curiously, KDM4A expression was only upregulated upon treatment with NVP-ADW742 and gefitinib, suggesting that while FYN upregulation is a general feature of drug tolerant cells, the mechanism of FYN upregulation may vary depending on the specific drug being used. Analysis of previously published RNA sequencing data from a series of osimertinib tolerant EGFR mutated lung cancer cell lines(39) revealed higher expression levels of FYN and KDM4A in the drug persisters, but not SRC (Fig. 5B). Consistently, we confirmed upregulation of FYN at both the protein and mRNA levels in gefitinib and osimertinib resistant PC9 and HCC827 cells (Figs. 5C-D). Pharmacological inhibition of FYN or downregulation of FYN expression through inhibition of KDM4 sensitized gefitinib resistant PC9 cells to EGFR inhibitor, suggesting that FYN-KDM4 are responsible for gefitinib resistant phenotype in this cell line (Figs.5E-G).

Figure 5.

Importantly, upregulation of FYN has been consistently observed in multiple independent studies involving drug-tolerant cancer cell lines and patient-derived xenografts treated with various drugs that have distinct target profiles, including TKIs (lapatinib, a HER2 inhibitor, against HER2 positive breast cancer(40)) and chemotherapy drugs (irinotecan, topoisomerase inhibitor against colorectal cancer(41); gemcitabine against pancreatic cancer(42); and carboplatin against high grade serous ovarian carcinoma(43)) (Fig. 5H). Moreover, enrichment of FYN has also been observed in residual disease following chemotherapy, including neoadjuvant chemostherapy plus bevacizumab treated HER2 negative breast cancer(44), neoadjuvant chemoradiotherapy combined plus atezolizumab treated esophageal cancer(45), and chemoradiotherapy treated colorectal cancer(46), indicating its potential role in mediating drug tolerance during chemotherapy (Fig. 5I). While the causal relationship between FYN expression drug tolerance in response to various therapeutic interventions warrants further study, these evidence suggest that FYN expression is associated with drug tolerance. Notably, an analogous increase in KDM4 was not consistently observed across all tumor models tested in Figures 5H-I (Fig. S8A-B). This suggests that, as previously noted in Figure 5A, while FYN serves as a general mediator of drug tolerance, the specific mechanisms underlying its upregulation may vary depending on the cancer type and the drug being administered. Taken together, these lines of evidence further support our findings in TNBC cell lines and suggest that FYN acts as a common mediator of drug tolerance.

Discussion

In this study, we employed combinatorial CRISPR screening to identify combinations of TKIs that exhibit synergistic effects in eliminating triple-negative breast cancer (TNBC). We discovered and validated that concurrent targeting of FYN, along with other tyrosine kinases such as IGF1R, EGFR or ABL2 can synergistically eradicate TNBC and impede cancer growth. Our findings also provide evidence that the transcriptional upregulation of FYN, facilitated by the activation of KDM4 histone demethylases, confers resistance and persistence to TKIs. Upregulation of FYN is a general feature of drug tolerant cancer cells, suggesting the association of FYN expression with drug resistance and tumor recurrence after treatment.

This research provides basis for breakthrough combinatorial therapy achieving effective targeted therapy with minimal risk of developing resistance. Our combinatorial CRISPR screening demonstrates that treatment with TKIs or histone demethylase inhibitors enhances the sensitivity of cells to other TKIs. Consequently, drug combinations exhibit a more potent inhibition of cancer growth than the simple sum of the therapeutic effects of individual drugs. Furthermore, synergistic drug combinations enable a reduction in the dosage of each drug, with minimal compromise in therapeutic efficacy. Such combinations yield a therapeutic response comparable to that achieved with significantly higher doses of each individual agent. We anticipate that combinatorial therapy has the potential to mitigate side effects by minimizing the dosage of each drug, thus widening the therapeutic window. Further studies should elucidate the downstream mechanisms by which FYN upregulation contributes to drug tolerance. SRC family kinases are known to upregulate multiple signaling pathways including ERK, AKT and p38 pathway(47). Although our study showed that, at least in MDA-MB-231 cell line, FYN depends on p38 activity for TKI resistance (figs. 2J-L), it should further be shown while this downstream mechanism is generalizable in other cancers. The downstream mechanism of p38 that contributes to drug resistance would also be of great interest to identify novel therapeutic approaches minimizing drug tolerance. Also, our combinatorial CRISPR screen results warrant further studies with other synthetic lethal gene combinations other than those involving FYN that are not deeply investigated in this study. Particularly, FGFR2, TEK, FRK identified as key nodes in figure 2C may be of particular interest as they are also associated with cancer cell survival(48, 49).

It is intriguing to observe that FYN is specifically upregulated in various models of drug resistance and tolerance. SRC family kinases, which includes FYN, have been linked to drug resistance(47, 50). In line with this, phosphoproteomic analysis of neoadjuvant chemotherapy resistant TNBC patient derived xenografts showed upregulated SRC family kinase networks including kinases and their substrates and adaptors(51). Our findings reveal that FYN is specifically upregulated at the mRNA level possibly through epigenetic regulations, providing further depth to our understanding of drug resistance in cancer therapy. The epigenetic reprogramming of the drug tolerant cells may be distinct depending on the tumor type or the therapeutic interventions, as KDM4A, which we show is increased upon TKI treatment in TNBC cell line in figure 3, is not significantly regulated in other cancers analyzed in figure 5. Therefore, the context dependent mechanisms underlying FYN upregulation, and its essentiality in constituting drug tolerance remains as subjects for further study.

Furthermore, our work highlights the significance of histone demethylases in TKI resistance. Numerous histone demethylases have been implicated in drug resistance and tolerance across different cancer drug types. For instance, the KDM5 family of H3K4 demethylases has been associated with the drug-tolerant persister phenotype against multiple TKIs(35). In our study, we identify KDM4 as a critical factor in the generation of drug-tolerant persisters in breast cancer. KDM4 is known to be upregulated in various cancers, including breast cancer, and promotes key malignant traits. Previous studies have demonstrated the essential role of KDM4 in induced pluripotency through its interaction with pluripotency factors(52). These findings suggest that an KDM4 inhibitor could be a promising therapeutic target with specific activity against cancer stem cells. Consistently, specific inhibitors targeting KDM4 have recently been developed and shown to inhibit the generation of breast cancer stem cells(38). The mechanisms underlying KDM4 upregulation upon drug treatment is currently unclear and remains as subjects for further study. Nevertheless, given our discoveries regarding the involvement of KDM4 in drug resistance in breast and lung cancer, the development of novel drugs targeting KDM4 holds significant therapeutic potential.

Materials and methods

Cell Culture

HEK293T, MDA-MB-231, Hs578T cells were obtained from American Type Cell Culture (ATCC) HCC1143, HCC1395, HCC1937 were obtained from Korean cell line bank. HEK293T and MDA-MB-231 were grown in DMEM supplemented with 10% FBS and penicillin/streptomycin. Hs578T and HCC1143, HCC1395, HCC1937 were grown in RPMI1640 supplemented with 10% FBS. Parental and gefitinib-, and Osimertinib-resistant PC9 and HCC827 cells were kind gifts from Jae Cheol Lee (Asan Medical Center, Seoul, Korea) and were grown in RPMI1640 supplemented with 10% FBS. Gefitinib and osimertinib resistant cells are maintained in the presence of 1μM gefitnib and 0.5μM osimertinib, respectively. Gefitinib resistant derivatives of PC9 and HCC827 cells were generated as described previously by treating cells with escalating dose of gefitinib (53). All cell culture medium and supplements are purchased from Welgene Inc.

Combinatorial library construction

Combinatorial library was constructed as previously described(15). The sgRNAs used in the screens were cloned in pAWp28 storage vector in two versions: one version containing human U6 driven sgRNA with wild type scaffold, and another containing mouse U6 driven sgRNA with cr2 variant scaffold. The sgRNA expression cassette consisting of U6 promoter and sgRNA were subject to one-pot, iterative cloning into lentiviral pTK799 vector using BglII-MfeI restriction sites flanking the sgRNA expression cassette and BamHI-EcoRI sites in pTK799. pTK799 vector is derived from pAWp12(15) by replacing CMV-GFP selectable marker to EFS-Puro.

Combinatorial CRISPR screening procedure

Lentivirus was generated in HEK293T cells by transfecting lentiviral transfer vector, and helper vectors (psPAX2, and pVSV-G) using Fugene HD (Promega). Lentiviral supernatant was collected 48 hours after transfection, and was frozen and stored in −80C. The appropriate titer for lentiviral transduction was determined by transducing MDA-MB231 cells with two-fold serial dilution of lentiviral supernatant, selecting with puromycin 2 days after transduction for 2 days, and determining cell viability with AQuaeous one cell viability, MTS assay (Promega). After determining the titer of lentiviral supernatant, 100 million MDA-MB231 cells carrying constitutively expressed Cas9 were transduced with CombiGEM library at MOI of 0.3. The expected initial coverage is 100 million x 0.3/ (54,289 different sgRNA combinations)= 553. Three days after transduction, the cells were either harvested as day 0 sample or selected with 2μg/mL puromycin (Invitrogen). Cells were treated with benzonase before harvesting to minimize carryover of plasmid DNA in lentiviral supernatant. Cells were grown in the presence of 2μg/mL puromycin for 20 days before harvesting.

The genomic DNA of harvested cells were isolated using Blood & Cell Culture Maxi kit (Qiagen). The PCR amplicon spanning the two sgRNAs were generated with PCR using Q5 High Fidelity DNA polymerase (New England Biolabs) and the following primers:

F: CAAGCAGAAGACGGCATACGAGAT CCTAGTAACTATAGAGGCTTAATGTGCG

R: AATGATACGGCGACCACCGAGATCTACAC NNNNNN ACACGAATTCTGCCGTGGATCCAA

The six nucleotides described as “NNNNNN” in reverse primer above represents unique index to identify biological replicates in multiplexed NGS run.

The PCR protocol involves 60 seconds of initial DNA denaturation at 98C, and 20 cycles of 10 seconds denaturation at 98C, 10 seconds annealing at 67C, and 120 seconds elongation at 72C. All genomic DNA isolated were used in PCR reaction at concentration of 40μg/mL. All PCR products were combined and precipitated with isopropanol at room temperature. The precipitated DNA was resuspended in 400μL EB buffer (Qiagen) and gel purified. The purified PCR products were sent for NGS by NextSeq500 paired end sequencing with the following sequencing primers:

Forward read: GGACTAGCCTTATTTGAACTTGCTATGCAGCTTTCTGCTTAGCTCTCAAAC

Forward index read: CGGTGCCACTTTTTCAAGTTGATAACGGACTAGCCTTATTTTAACTTGCTATTTCTAGCTCTAAAAC

Reverse read: GCA CCG AGT CGG TGC TTT TTT GGA TCC ACG GCA GAA TTC GTGT

The raw GI scores calculated as deviation from the quadratic fit of the expected-observed Z score plot. The GI scores were normalized by dividing the raw GI scores with the standard deviation of the GI scores obtained from the 200 nearest neighbors in terms of expected Z scores (Fig. S1I)(11).

Data analysis

The sgRNA sequences were identified and their occurrences were counted with C++ script deposited in Github (https://github.com/tackhoonkim/combinatorial-CRISPR-screens-2023).

Validation of screens using sgRNAs

Individual sgRNA was cloned to either pTK1329, and pTK1336 that are both derived from pAWp12 with EFS-GFP and EFS-mCherry, respectively, as selectable markers. Validation of synthetic lethality between gene A and B were analyzed by transducing MDA-MB-231 Cas9 cells with four combinations of lentiviral supernatant pairs (MOI ∼ 0.5 each) containing (1) GFP-sgA and mCherry-sgB; (2) GFP-sgA and mCherry-sgCon; (3) GFP-sgCon and mCherry-sgB; (4) GFP-sgCon and mCherry-sgCon. The fraction of GFP/mCherry double positive cells were analyzed using BD Accuri C6 and its accompanying software. The expected fold change in sgA+sgB were calculated as FC_{sgA+sgCon x} FC_sgCon+sgB, where FC is normalized fold change in fraction of GFP/mCherry double positive cells relative to those transduced with GFP-sgCon and mCherry-sgCon.

MTT Cell viability assay

Cells were seeded at 1000-2000 cells/well in 96 well plate. Tyrosine kinase inhibitors at indicated combination of dose were treated 12 hours after seeding, and cells were grown for 3 days. The relative viability was measured by EzCytox cell viability assay (Dojindo). The absorbance at 450nm wavelength was measured using EnVision multimode plate reader (PerkinElmer).

Cell death and cell proliferation assay

Cells were incubated with tyrosine kinase inhibitors for 48 hours. Cell proliferation was quantified with BrdU assay using FITC conjugated BrdU antibody (Biolegend, 364103) and propidum iodide/RNase A solution (Cell Signaling), analyzed with BD Accuri C6 and accompanied software. Cell death was quantified with Live-Dead cell staining kit (Molecular Probes) by flow cytometry analysis using BD Accuri C6 and accompanied software.

Western blot analysis

Cells were treated with drugs for 48 hours unless otherwise indicated. Western blots were performed as previously reported. Briefly, Cells were lysed in RIPA buffer (50mM Tris-HCl pH 7.5, 150mM sodium chloride, 0.1% sodium dodecyl sulfate, 0.5% sodium deoxycholate, 1% NP-40, 1mM EDTA) supplemented with protease inhibitor (aprotinin, leupeptin, pepstatin A, and phenylmethylsulfonyl fluoride [PMSF]) and phosphatase inhibitor (sodium fluoride and sodium orthovanadate) cocktail. Antibodies used for western blot analysis were: anti-SRC (Santa Cruz biotechnology, mouse sc-), anti-FYN (Cell Signaling Technology, rabbit #4023), anti-phospho-FYN Y530 (Invitrogen, PA5-36644), anti-GAPDH (Cell Signaling Technology, rabbit #5174), anti-KDM4A (Bethyl Laboratories, rabbit A300-861A), anti-phospho-ERK T202/Y204 (Cell Signaling Technology, rabbit #9101), anti-ERK (Cell Signaling Technology, rabbit #9102), anti-phospho-STAT3 Y705 (Cell Signaling Technology, rabbit #9145), anti-STAT3 (Cell Signaling Technology,), anti-phospho-AKT (Cell Signaling Technology, mouse #4051), anti-AKT (Cell Signaling Technology,), anti-phospho-p38 (Cell Signaling Technology,), anti-p38 (Cell Signaling Technology,).

RT-qPCR analysis

RNA is extracted from cultured cells with Trizol (Invitrogen) according to the manufacturer’s instructions. The precipitated RNA pellet was resuspended in RNase free water (Enzynomics), and was subject to reverse transcription with M-MLV reverse transcriptase (Enzynomics) at 37C for 2 hours, followed by heat inactivation at 95C for 5 minutes. The resulting cDNA was used for quantitative real time PCR using TOPreal SYBR Green qPCR premix (low ROX, Enzynomics) reagent and CFX96 Real-Time PCR detection system (Bio-Rad).

Xenograft assay

All animal experiments were approved by IACUC of Korea Institute of Science and Technology (KIST). Six-week-old female nude mice were injected with 5 x 10⁶ MDA-MB-231 cells suspended in 1:1 (w/w) mixture of PBS and growth factor reduced Matrigel (Corning) in fourth inguinal mammary fat pad. Starting two weeks after tumor cell injection, saracatinib (50mg/kg mouse body weight, MedChemExpress), NVP-ADW742 (20mg/kg, Sigma), gefitinib (20mg/kg, MedChemExpress), QC6352 (10mg/kg, MedChemExpress) in 45% saline+40% polyethyleneglycol 300 (sigma)+5% Tween-80 (sigma)+5% DMSO (sigma) were injected intraperitoneally every 24 hours for two weeks. Tumor volume was measured by digital caliper and calculated as (width)² x length x 0.5.

Public database analyses

Gene Expression Omnibus (GEO) data with breast cancer cohort (GSE25066(24)) were analyzed using web based platform Cancer Target Gene Screening (https://ctgs.biohackers.net)(54). Cancer Cell Line Encyclopedia (CCLE) data were analyzed using depmap R package version 1.14. The list of GEO data used for analysis are listed in table S2.

Primary TNBC organoid culture and drug treatment

Specimens was obtained from enrolled patients with TNBC breast cancer with IRB approval. Tumor tissue was collected and transferred using cold RPMI1640 media at the National Cancer Center (Goyang, Republic of Korea). Then the tissues were dissected with a blade on Petri dish and enzymatically digested with dissociated kit (Multi Tissue Dissociation Kit 1, 130-110-201) by gentle MACS Dissociators (Miltenyi Biotec, Germany). After cell counting, 1 × 10⁵ cells were embedded in 40 μl of Matrigel (Corning) and seeded into each well of a 24-well cell culture plate. After the matrigel was solidified, 500 μL organoid medium supplemented with advanced DMEM/F12 medium (Invitrogen, USA), B27 (Invitrogen, USA, 17504), 1.25 mM N-acetylcysteine (Sigma, USA, A9165), 5 ng/mL EGF (PeproTech, USA, AF-100-15), 20 ng/mL FGF-10 (Peprotech, 100-26), 5ng/ml FGF7 (Peprotech, AF-100-19), 50ng/ml R-spondin 1 (Qkine, Qk006), 5 mM nicotinamide (Sigma, N0636), 500 nM A83-01 (Tocris, 2939), 1X GlutaMAX (Gibco, 35050-061), 100ug/ml primocin (Invivogen, ant-pm), 10mM HEPES (Gibco, 15630-080), 1X Noggin (U-Protein Express BV, N002), 1X ITS (Gibco, 12585014), 100nM β-estradiol (Sigma, E2758), 1ug/ml Hydrocortisone (Sigma, H0888), 5nM Heregulin (PeproTech, AF-100-03), 500nM SB202190 (R&D system, 1264), 10uM Y27632 (TOCRIS, 1254) as described by Clevers and colleagues (55), was added to each well and the cells grown under standard culture conditions (37 ℃, 5% CO2).

Availability of data and materials

The NGS data for CRISPR screening results are available under NCBI SRA accession code PRJNA976939.

Acknowledgements

We thank BioMicro Center of MIT for sequencing analysis. We thank Jae Cheol Lee (Asan Medical Center, Korea) for providing gefitinib resistant lung cancer cell lines. This work was supported by Korea Institute of Science and Technology (KIST) Institutional Programs (2E32331 to T.K.); and National Research Foundation of Korea, funded by the Korean government (MSIT) (2021R1A2C1093499 to T.K., 2020M3A9A5036362 to S.Y.K).

Additional information

Author contributions

T.K., B.-S.P., H.J. and J.K. performed experiments. T.K. and T.K.L. supervised the research. S.H. S.-Y.J. and S.Y.K. performed experiments with TNBC patient derived organoids. D.K., S.K.L. generated and provided osimertinib resistant HCC827 cell line.

Ethics Statement

All experiments with human tumor organoids were conducted in accordance with the requirements of the National Cancer Center Institutional Review Board (IRB).

Additional files

Supplementary dataset S2

Supplementary dataset S2

Supplementary figures S1-8, tables S1-3