YY1 downregulation underlies therapeutic response to molecular targeted agents

doi:10.21203/rs.3.rs-4595409/v1

Download PDF

Article

YY1 downregulation underlies therapeutic response to molecular targeted agents

https://doi.org/10.21203/rs.3.rs-4595409/v1

This work is licensed under a CC BY 4.0 License

Journal Publication

published 27 Nov, 2024

Read the published version in Cell Death & Disease →

You are reading this latest preprint version

During targeted treatment, oncogene-addicted tumor cells often evolve from initial drug-sensitive state through a drug-tolerant persister bottleneck towards the ultimate emergence of drug-resistant clones. The molecular basis underlying this therapy-induced evolutionary trajectory is not completely elucidated. Here, we employed a multifaceted approach and implicated a convergent role of transcription factor Yin Yang 1 (YY1) in the course of diverse targeted kinase inhibitors. Specifically, pharmacological perturbation of receptor tyrosine kinase (RTK)/mitogen-activated protein kinase (MAPK) pathway resulted in the downregulation of YY1 transcription, which subsequently resumed upon therapeutic escape. Failure to decrease YY1 subverted cytotoxic effects, whereas eliminating residual YY1 maximized anticancer efficacy and forestalled the emergence of drug resistance. Mechanistically, YY1 was uncovered to dictate cell cycle and autophagic programs. Immunohistochemical analysis on a wide spectrum of clinical specimens revealed that YY1 was ubiquitously expressed across lung adenocarcinomas and exhibited anticipated fluctuation in response to corresponding RTK/MAPK inhibition. These findings advance our understanding of targeted cancer management by highlighting YY1 as a determinant node in the context of genotype-directed agents.

Biological sciences/Cancer/Cancer therapy/Targeted therapies

Biological sciences/Molecular biology/Transcription/Transcriptional regulatory elements

Stepwise progression to drug resistance represents a major challenge in anticancer treatment targeting driver oncoproteins such as EGFR, ALK, KRAS, BRAF, and many others ¹. Initially, presumed lethal therapeutic insults often yield dramatic clinical responses. Nevertheless, a reservoir of drug-tolerant persister cells, commonly termed minimal residual disease, invariably manage to survive and eventually result in tumor relapse ^{2, 3}. Throughout this evolutionary process, an increasing variety of genetic ^{4, 5}, epigenomic ^{6, 7}, transcriptional ^{8, 9}, translational ^{10, 11}, post-translational ^{12, 13}, and metabolic ^{14, 15} mechanisms operate to underpin the transition and maintenance of discrete stages, depending on specific cancers and therapies. Such a multifactorial and heterogeneous regulatory landscape poses significant obstacles to effective management and durable control of recalcitrant malignancies ¹⁶. Further in-depth understanding of molecular profiles at different layers and phases may provide new insights into convergent events underlying how neoplastic cells respond and evade targeted agents.

Chromatin accessibility dynamics perhaps constitutes one of the most relevant but overlooked genomic characteristics corresponding to the meticulous changes following treatment with targeted therapies ^{17, 18}. The phenotypic alterations associated with the sequential drug-sensitive, drug-tolerant, and drug-resistant switch probably require widespread remodeling of open chromatin architecture and consequent rewiring of gene expression programs. Indeed, mounting evidence implicates unique chromatin states in the response, survival, and resurgence of cancer cells that are determined by varied epigenetic modifiers ^{6, 19, 20, 21, 22, 23}. However, additional key players involved in this process remain to be elucidated for developing rational therapeutic strategies.

Here, we integrated the assay for transposase-accessible chromatin using sequencing (ATAC-seq) and transcriptome-wide RNA-sequencing (RNA-seq) to identify Yin Yang 1 (YY1) as a pivotal transcription factor in the context of diverse molecular therapeutics targeting receptor tyrosine kinase (RTK)/mitogen-activated protein kinase (MAPK). YY1 was transcriptionally downstream of RTK/MAPK signaling, and governed the anticancer effects of pathway blockade by regulating cell cycle and autophagic flux. These mechanistic findings illuminate a general hub underlying drug-induced evolutionary trajectory in oncogene-addicted tumor cells, which holds immense promise for novel intervention opportunities.

Characterization of chromatin accessibility dynamics implicates YY1 in EGFR inhibitor response

We modeled the drug-sensitive (DS), drug-tolerant (DT), and drug-resistant (DR) continuum by using the clinically approved EGFR inhibitor erlotinib to treat a well-studied EGFR-mutant lung cancer cell line PC9, which was subjected to ATAC-seq and RNA-seq in parallel (Fig. 1A). Principal component analysis unveiled a comparable pattern of these different states in ATAC-seq and RNA-seq results, with DT cells being clearly separated from DS and DR cells (Fig. 1B). The above observation was confirmed by volcano plots of differential chromatin accessibility (Supplementary Fig. 1A) and gene expression (Supplementary Fig. 1B). ATAC-seq profiling identified seven distinctive clusters of accessible DNA loci (Supplementary Fig. 2A), which were presumably occupied by diverse transcription factors (Supplementary Fig. 2B). In addition, we performed motif enrichment analysis on the entire set of ATAC-seq peaks to infer candidate functional transcription factors and filtered them by querying the corresponding RNA-seq data (Fig. 1C). Based on this integrated approach, a group of transcription factors displaying dynamic changes throughout targeted treatment were selected for a focused CRISPR-Cas9 screen. YY1, an evolutionarily conserved GLI-Krüppel zinc finger transcription factor ²⁴, conferred cell suppression upon genetic depletion (Fig. 1D). We leveraged the CUT&Tag technology (Supplementary Fig. 3) to verify that genome-wide YY1 binding underwent a marked decrease in DT cells compared to DS cells and was completely restored in DR cells (Fig. 1E). Therefore, YY1 might partially account for kinetic alterations of chromatin openness and serve as a potential regulator in EGFR inhibitor response.

EGFR regulates YY1 expression through the MAPK signaling pathway

Correlated with fluctuating YY1 occupancy following EGFR inhibitor treatment, YY1 protein abundance notably declined in DT cells relative to DS or DR cells, which was ascribable to altered YY1 gene transcription (Fig. 2A). Corroborating the pharmacological evidence, genetic EGFR depletion likewise resulted in YY1 downregulation at both mRNA and protein levels, whereas YY1 ablation did not affect EGFR expression (Fig. 2B). Moreover, immunoblotting assays revealed that YY1 intensity fully emulated EGFR phosphorylation when PC9 cells were exposed to the first-, second-, or third-generation EGFR inhibitors in a time course fashion (Fig. 2C). Similar associations were recapitulated in various EGFR-mutant lung cancer cell lines including HCC827, NCI-H1975, and NCI-H820 (Supplementary Fig. 4A). Immunofluorescence staining indicated predominant nuclear localization of YY1, which showed diminished signals consequent to EGFR blockade (Fig. 2D). MAPK and PI3K signaling cascades represented two major pathways downstream of EGFR kinase to possibly regulate YY1 expression. We sought to dissect their specific contributions by administering trametinib (MEK inhibitor), ulixertinib (ERK inhibitor), pictilisib (PI3K inhibitor), or ipatasertib (AKT inhibitor) to block either axis (Fig. 2E). In PC9 cells, applying trametinib or ulixertinib was sufficient to induce YY1 downregulation while pictilisib or ipatasertib had no equivalent effects (Fig. 2F), supporting an exclusive role of MAPK but not PI3K pathway. Such a notion was substantiated by validation experiments in HCC827, NCI-H1975, and NCI-H820 cells (Supplementary Fig. 4B). Analogous to erlotinib, trametinib also attenuated YY1 mRNA expression (Supplementary Fig. 4C). Thus, our data unambiguously proved that EGFR regulated YY1 expression through the MAPK signaling pathway.

RTK/MAPK inhibition downregulates YY1 in diverse oncogene-addicted cancer models

We reasoned that YY1 function might not be restricted to EGFR-mutant lung cancer. Indeed, YY1 gene expression was unrelated to EGFR status in lung adenocarcinoma from the Cancer Genome Atlas (Fig. 3A). In addition, higher levels of YY1 mRNA tended to predict inferior patient survival irrespective of EGFR alterations (Fig. 3B). At the protein level, immunoblotting assays illustrated that YY1 was readily detectable in a wide spectrum of oncogene-driven lung cancer cell lines (Fig. 3C), as well as most primary tumor samples regardless of EGFR mutation (Fig. 3D). Based on these observations, a panel of lung cancer cell lines bearing heterogeneous RTK/MAPK aberrations were assembled and treated with corresponding kinase inhibitors. YY1 abundance was appreciably decreased along with ERK repression in a time-dependent manner (Fig. 3E). We then extended our investigation to other cancer types with RTK/MAPK pathway dysregulation. As two examples, erdafitinib (FGFR inhibitor) and sotorasib (KRAS G12C inhibitor) impaired YY1 expression in genotype-matched bladder cancer cell line RT112 and pancreatic cancer cell line MIA PaCa-2, respectively (Fig. 3F). We concluded that RTK/MAPK inhibition downregulated YY1 in diverse oncogene-addicted cancer models.

YY1 depletion underlies therapeutic effects of molecular targeted agents

We wondered whether YY1 exerted functional activities, especially in the context of molecular targeted regimens. Initially, lentiviral CRISPR-Cas9 technology was employed to knock out YY1 in multiple EGFR-mutant, KRAS-altered, or ALK-rearranged lung cancer cell lines using two independent sgRNAs (Fig. 4A), and cell viability was significantly inhibited without exception (Fig. 4B). Subsequently, we genetically manipulated YY1 expression at discrete phases of EGFR inhibitor treatment and assessed the impact on therapeutic outcome (Fig. 4C). Firstly, YY1 ablation in DS cells could improve drug sensitivity by preventing DT emergence in PC9 (Fig. 4D), as well as HCC827, NCI-H1975, and NCI-H820 (Supplementary Fig. 5A). Secondly, YY1 overexpression in DS cells antagonized drug response by accelerating DT formation in PC9 (Fig. 4E), as well as HCC827, NCI-H1975, and NCI-H820 (Supplementary Fig. 5B). Thirdly, YY1 knockout in DT cells, leading to drastically compromised proliferation estimated by EdU incorporation assays (Fig. 4F), eliminated residual colonies and overcame DR expansion (Fig. 4G). Finally, we conducted in vivo experiments to confirm the in vitro findings. Upon YY1 loss, PC9 xenograft growth was slowed and more susceptible to erlotinib inhibition (Fig. 4H), yielding reduced tumor masses (Fig. 4I) with less weight (Fig. 4J). In contrast, exogenous YY1 expression attenuated erlotinib efficacy (Fig. 4K), as exemplified by larger tumor masses (Fig. 4L) with gained weight (Fig. 4M). Collectively, these studies suggested that YY1 depletion underlay therapeutic effects of molecular targeted agents.

YY1 dictates cell cycle and autophagic programs

To determine the mechanistic underpinnings of YY1 function, RNA-seq was performed to compare YY1-null and parental PC9 cells (Supplementary Fig. 6A). As a transcription factor, YY1 knockout resulted in a considerable number of differentially expressed genes (DEGs) (Fig. 5A; Supplementary Fig. 6B). There was a significant positive correlation between fold changes induced by two sgRNAs at the transcriptome-wide scale (Supplementary Fig. 6C), and Venn diagram illustrated that 2835 out of 5811 DEGs (48.8%) were shared (Supplementary Fig. 6D). Gene ontology annotation pinpointed that cell cycle-related terms were downregulated while stress- and autophagy-related pathways were upregulated (Fig. 5B). Gene set enrichment analysis (Fig. 5C) and Metascape algorithm (Supplementary Fig. 7A) validated the enriched biological processes associated with YY1 ablation. As the experimental proof, cell cycle examination by flow cytometry uncovered that YY1-depleted PC9 cells were arrested at the G2/M phase (Fig. 5D). A closer inspection on the autophagic machinery found that in the absence of YY1 ^{25, 26}, many regulators were widely altered (Supplementary Fig. 7B), among which early autophagy-related genes were generally upregulated whereas late autophagy-related genes were frequently downregulated (Supplementary Fig. 7C). LC3-II, a commonly used autophagy indicator, was markedly accumulated in PC9 (Fig. 5E), as well as NCI-H1975, NCI-H23, and NCI-H3122 (Fig. 5F). Of note, similar phenomenon was observed in chloroquine-treated PC9 cells, in line with autophagic flux suppression. Consistently, the tandem RFP-GFP-LC3 probe revealed a rise in autophagosomes (RFP⁺GFP⁺ puncta) rather than acidified autolysosomes (RFP⁺GFP⁻ puncta) (Fig. 5G). Morphometric transmission electron microscopy identified abundant autophagosomes but few autolysosomes upon YY1 depletion, suggestive of deficient autophagosome-lysosome fusion (Fig. 5H). These data supported that YY1 might affect therapeutic response to molecular targeted agents by dictating cell cycle and autophagic programs.

YY1 exhibits anticipated fluctuation following targeted treatment in lung cancer patients

To investigate the clinical relevance of YY1 expression, three cohorts of lung adenocarcinomas were collected. First, immunohistochemical interrogation of 89 EGFR-mutant, 51 KRAS-altered, and 46 ALK-rearranged lung cancer specimens unveiled that YY1 was ubiquitously expressed in malignant cells at an elevated level relative to normal epithelium (Fig. 6A; Supplementary Table 1). Second, YY1 was evaluated in four lung cancer patients who harbored somatic EGFR mutations and received off-label neoadjuvant targeted therapies (Fig. 6B; Supplementary Table 2). Although YY1 was highly expressed in tumor lesions compared with adjacent normal tissues at baseline, consistent reduction was observed in post-treatment versus pre-treatment samples (Fig. 6C). Third, YY1 was analyzed in six cases of advanced EGFR-mutant lung adenocarcinoma with disease relapse following first-line EGFR inhibitor treatment (Fig. 6D; Supplementary Table 3), which showed that YY1 levels rebounded to baseline in recurrent tumors (Fig. 6E). Taken together, YY1 was ubiquitously expressed and exhibited anticipated fluctuation following targeted treatment in lung cancer patients.

This study presented a global map of chromatin accessibility and transcriptomic landscape during EGFR perturbation. Integrated bioinformatics and functional analyses highlighted a model implicating transcription factor YY1 as a master regulator downstream of RTK/MAPK signaling. We showed how YY1 might govern antineoplastic output by dictating cell cycle and autophagic programs. These discoveries revealed an exciting and previously unappreciated component in charge of therapeutic response to molecular targeted agents, and pointed to future avenues for improving the magnitude and duration of clinical benefit.

Despite the proven significance and current limitations of targeted treatment to reducing cancer mortality, our understanding of therapy-induced evolutionary trajectory remains surprisingly incomplete ²⁷. Recent literature has individually focused on persister or refractory cells, both constituting major causes of disease relapse ^{2, 3, 16}. Using EGFR-mutant lung cancer as a representative system, we extended prior investigations in two dimensions to address this unsolved fundamental challenge. First, the whole progressive course upon drug intervention was modeled across the continuum of drug-sensitive, drug-tolerant, and drug-resistant phases to approximate a typical scenario in clinic. Second, ATAC-seq and RNA-seq were combined to thoroughly profile the molecular dynamics at different layers. Such a comprehensive interrogation allowed to identify that YY1 underwent kinetic alterations of genomic occupancy and gene expression. Of special importance, YY1 was found to be not only modulated by EGFR but broadly controlled by RTK/MAPK signaling at the transcriptional level. Thus, our research pinpointed a universal program operating in the course of diverse kinase inhibitors targeting the RTK/MAPK cascade to treat oncogene-driven malignancies. It is noteworthy that YY1 could not be fully eliminated even with prolonged drug exposure, implying that additional regulatory modes may exist and warrant further exploration.

YY1 belongs to the GLI-Krüppel family of zinc finger transcription factors ²⁴. It is highly expressed in many tumor types and regulates numerous genes involved in hallmarks of cancer including uncontrolled cell proliferation, invasive behavior, and metabolic reprogramming ^{28, 29, 30}. Along this line, our data unraveled that cell cycle and autophagic flux, among a plethora of other pathways, were evidently impacted by YY1 ablation, which offered mechanistic interpretations of YY1 function in anticancer treatment. Moreover, YY1 can act as a transcriptional activator or repressor depending on the context. Indeed, we discovered that YY1 exerted dichotomous effects on early or late autophagy-related genes, aggregately leading to defective autophagic progression. Beyond its role as a traditional transcription factor, YY1 is reported to promiscuously interact with cofactors and chromatin modifiers ³¹, mediate enhancer-promoter looping ³², and possibly form phase-separated condensates ³³. Therefore, its exact mechanism of action underlying targeted therapeutics needs future in-depth dissection.

One critical question concerns whether the preclinical observations recapitulate the clinical reality in cancer patients. Notably, we collected three cohorts of lung adenocarcinomas to resolve this issue. At baseline, YY1 was ubiquitously detected across a total of 186 primary tumors spanning EGFR-mutant, KRAS-altered, and ALK-rearranged specimens. In four EGFR-mutant subjects receiving EGFR inhibitors and longitudinal sampling, there was a consistent decrease of YY1 protein in paired post-treatment versus pre-treatment sections. Finally, YY1 expression was invariably resumed upon disease recurrence following first-line EGFR inhibitor treatment in six EGFR-mutant patients. Collectively, we demonstrated that YY1 levels determined cytotoxic response to molecular targeted agents, providing a potential biomarker for evaluating drug effectiveness. In addition, our work establishes a sound rationale to develop combination regimens that eradicate YY1 remnant at drug-tolerant state or prohibit YY1 recovery during drug-resistant transit.

Cell culture and reagents

The tumor cell lines and HEK293T were originally obtained from the American Type Culture Collection (ATCC) or Japanese Collection of Research Bioresources Cell Bank (JCRB), where mycoplasma contamination was routinely tested and cell identity was monitored by short tandem repeat (STR) profiling. Drug-tolerant persister cells were generated through continuous treatment with 1 µM of erlotinib for 10 days, according to protocols described previously ⁶. Drug-resistant cells were generated by overexpressing the EGFR^T790M mutant in PC9 cells, and then maintained in 1 µM erlotinib. Cells were cultured in RPMI1640 (Life Technologies) supplemented with 10% fetal bovine serum (Gibco), l-glutamine (2 mM), penicillin (100 units/mL), and streptomycin (100 µg/mL). Small-molecule inhibitors were purchased from Selleck Chemicals or MedChemExpress, and reconstituted in DMSO (Sigma-Aldrich) at a stock concentration of 10 mM. The following inhibitors were used at these final concentrations unless otherwise indicated: erlotinib (1 µM), afatinib (4 µM), osimertinib (4 µM), trametinib (5 µM), ulixertinib (1 µM), pictilisib (1 µM), ipatasertib (1 µM), alectinib (5 µM), capmatinib (4 µM), PLX8349 (4 µM), erdafitinib (2 µM), sotorasib (1 µM), chloroquine (50 µM). For visualization, cells were fixed with formalin and stained with crystal violet.

Plasmids and sgRNAs

Plasmids for gene overexpression were constructed using the Gibson Assembly Cloning Kit (New England Biolabs) and Gateway Cloning System (Invitrogen). EGFR mutations were generated using the Q5 Site-Directed Mutagenesis Kit (New England Biolabs) and verified by Sanger sequencing. The CRISPR-Cas9 system was employed to knock out indicated genes. The primers for cloning and sgRNA sequences were provided in Supplementary Table 4.

Virus production and cell infection

For virus production, HEK293T cells in a 10-cm dish were co-transfected with 5 µg of lentiviral constructs, 5 µg of plasmid Δ8.9, and 3 µg of plasmid VSVG using Lipofectamine 2000. Cells were incubated at 37°C and the medium was replaced with fresh complete medium after 12 h. Virus-containing medium was collected at 48–72 h after transfection and supplemented with 8 µg/mL polybrene (Fluka) to infect target cells in 6-well dishes. Infected cells were selected with 2–5 µg/mL puromycin or blasticidin for one week.

Western blotting analysis

Cells were lysed in RIPA buffer (50 mM Tris pH 7.4, 150 mM NaCl, 1% NP-40, 0.1% SDS, 2 µM EDTA) containing protease inhibitors (Roche) and phosphatase inhibitors (Roche). Protein concentrations were quantified using Pierce BCA Protein Assay Kit (Thermo Fisher Scientific). The cell lysates (~ 20 µg protein) were subjected to SDS-PAGE (Invitrogen) and Western blot. The following primary antibodies were used: YY1 (ab109237, Abcam), phospho-EGFR (Y1068) (#48576, Cell Signaling Technology), EGFR (#4267, Cell Signaling Technology), phospho-ERK (T202/Y204) (#9106, Cell Signaling Technology), ERK (#4695, Cell Signaling Technology), phospho-AKT (T308) (#2965, Cell Signaling Technology), AKT (#2966, Cell Signaling Technology), LC3-II (#12741, Cell Signaling Technology), β-actin (#5125, Cell Signaling Technology).

Immunofluorescence staining

Cells were fixed with 4% paraformaldehyde for 15 min, and permeabilized with 0.1% Triton X-100 in PBS for 10 min. After three PBS washes, cells were blocked with 2% BSA in PBS for 30 min at room temperature (RT), and incubated with primary antibodies against YY1 (#66281-1-Ig, Proteintech) diluted in 2% BSA at 4°C overnight. Cells were incubated with Alexa Fluor 488-labeled anti-mouse IgG (A11029, Invitrogen) and Alexa Fluor 594-labeled anti-rabbit IgG (A11037, Invitrogen) for 1 h at RT in the dark, followed by 4',6-diamidino-2-phenylindole (DAPI) (Invitrogen) counterstaining for 5 min. The staining images were acquired using a confocal laser scanning microscope (Leica). For RFP-GFP-LC3 analysis, PC9 cells were transfected with 2µg of tandem monomeric RFP-GFP-LC3 plasmid for 48 h and subjected to the indicated treatment. For evaluating tandem fluorescent LC3 puncta, the cells were rinsed once with PBS followed by confocal microscopy (Leica) analysis. The number of LC3 puncta per cell was counted using Image J (v 2.1.0).

EdU incorporation assay

The EdU (5-ethynyl-2'-deoxyuridine) incorporation assay was performed with the Cell-Light™ EdU DNA Cell Proliferation Kit (RiboBio) according to the manufacturer’s protocol. Briefly, cells were treated as indicated, incubated with EdU (50 µM) for 2 h, and fixed in 4% paraformaldehyde at RT for 30 min. The cells were stained with ApolloGreen fluorescent dye, followed by incubation with Hoechst reaction solution. Stained samples were viewed under a fluorescence microscope (Leica).

Patient samples

Human samples were obtained in accordance with ethical guidelines of U.S. Common Rule, and the study was approved by the Ethics Committee of Ren Ji Hospital. Written informed consent was acquired from all patients. Paired lung adenocarcinoma samples were collected before and after neoadjuvant targeted therapy, or at baseline and upon disease recurrence following first-line EGFR inhibitor treatment. In addition, we assembled two cohorts of treatment-naïve lung adenocarcinomas. One cohort contained 18 patients, whose tissue specimens were subjected to Western blotting analysis. The other cohort contained 186 patients with driver gene mutations for immunohistochemistry staining of the formalin-fixed and paraffin-embedded (FFPE) sections.

Immunohistochemistry staining

The tissue slides were baked, dewaxed with xylene, passed through graded alcohols, and antigen retrieved with 10 mM citric sodium (pH 6.0) in a steam pressure cooker for 20 min. The slides were then treated with 3% hydrogen peroxide solution in methanol for 10 min to quench endogenous peroxidase activity, blocked with goat serum and incubated with primary antibodies against YY1 (ab81552, Abcam), followed by incubation with horseradish peroxidase-conjugated secondary antibody for 1 h at RT. Antigen visualization was performed using 3,3’-diaminobenzidine (DAB) chromogen (Vector Laboratories). Slides were counterstained with hematoxylin, dehydrated and cover-slipped with mounting solution (Invitrogen). Whole slides were scanned with a Leica Aperio CS2 slide scanner system (Leica Biosystems). The staining was assessed independently by two pathologists without knowledge of patient characteristics based on the H-score method. The H-score was calculated by adding the percentage of positive cells multiplied by an ordinal value corresponding to the intensity level (none = 0, weak = 1, moderate = 2, strong = 3). H-scores ranged from 0 to 300, and tissue samples were further defined as negative (0–49), weak (50–99), medium (100–199), or positive (200–300).

Cell cycle analysis

The cell cycle was measured using iodide (PI) staining and flow cytometry analysis. Briefly, genetically edited PC9 cells were collected and washed once with PBS. The cells were then fixed with cold 70% ethanol for 30 minutes and resuspended in Propidium Iodide (PI)/RNase Staining Solution (Cell Signaling Technology). FACS AriaII cytometer (BD Biosciences) was used for the flow cytometry analysis and the data was processed with Flowjo software.

Transmission electron microscopy

For transmission electron microscopy (TEM), PC9 cells were treated as indicated, washed twice with PBS, and then fixed for 6 h at RT in 2.5% ultrapure glutaraldehyde in PBS. Postfixation was done by 1% osmium tetroxide for 90 min at 4°C, before washing four times in PBS and dehydrating with gradient ethanol solutions (Sinopharm Chemical Reagent) from 50–100% in a 10% graded series. Infiltration was carried out using ethanol with propylene oxide (1:1 ratio) for 30 min. The specimens were embedded in Epon 812 resins (Ted Pella), followed by polymerization at 60°C for 48 h. Ultrathin sections were cut to a thickness of 70 nm by Leica EM UC7 ultramicrotome and stained with saturated uranyl acetate in 50% ethanol, followed by Reynolds lead citrate. Electron microscopy images were obtained using a TEM system (FEI Tecnai G2 Spirit BioTwin).

RNA extraction and quantitative PCR assay

Total RNA from cells was extracted with RNA Isolation Kit (Vazyme Biotech) and converted to cDNA using the High-Capacity cDNA Reverse Transcription Kit (Invitrogen). Quantitative real-time PCR was performed on a ViiA™ 7 Real-Time PCR System (Applied Biosystems) using ChamQ Universal SYBR qPCR Master Mix (Vazyme Biotech). All samples were normalized to β-actin expression as the endogenous control. At least three biological replicates were included for each condition. The primer sequences for PCR were provided in Supplementary Table 4.

RNA sequencing and analysis

PC9 cells were genetically edited or treated as indicated. Total RNA was purified using the RNeasy Plus kit (Qiagen) following the manufacturer's instructions. RNA quality was assessed by NanoDrop 8000 (Thermo Fisher Scientific) and agarose gel electrophoresis. Sequencing libraries were prepared using the NEBNext Ultra RNA Library Prep Kit for Illumina (NEB). Clustering of the index-coded libraries on a cBot Cluster Generation System using TruSeq PE Cluster Kit v3-cBot-HS (Illumina), and the library preparations were sequenced on an Illumina Novaseq platform to generate 30 million 150 bp paired-end reads (Novogene). All the downstream analyses were based on the clean data which were obtained by removing low quality reads and reads containing adapters or ploy-N sequences. The index of the reference genome was built using Bowtie (v2.2.3) ³⁴, and clean reads were aligned to the reference genome using HISAT2 (v2.0.5) ³⁵. FeatureCounts (v1.5.0-p3) was used to count the reads numbers mapped to each gene ³⁶. Differential expression analysis was performed using the DESeq2 (v1.20.0) R package ³⁷. Transcripts with adjusted P values of < 0.05 were assigned as differentially expressed genes.

ATAC sequencing

The assay for transposase-accessible chromatin using sequencing (ATAC-seq) was performed as previously described ³⁸. First, a total of 5 × 10⁴ cells were harvested in ice-cold washing buffer (10 mM Tris-HCl pH 7.4, 10 mM NaCl, 3 mM MgCl₂, 0.1% IGEPAL CA-630). Cell pellets were resuspended in Tn5 transposition reaction mix containing 25 µL 2× TD Buffer, 2.5 µL Tn5 Transposes (Illumina) and 22.5 µL nuclease-free water, and incubated at 37°C for 30 min. DNA was purified with MinElute PCR Purification Kit (Qiagen). Eluted DNA was barcoded and amplified using NEBNext Q5 Hot Start HiFi PCR Master Mix (#M0543L, New England Labs) in a total volume of 50 µL with the following PCR program: 72°C for 3 min; 98°C for 30 s; 12 cycles of 98°C for 30 s, 63°C for 30 s and 72°C for 3 min; 72°C for 5 min. The libraries were purified with QIAquick PCR Purification Kit (Qiagen) and sequenced on the Illumina HiSeq with 50-bp paired-ends. Reads were aligned to the human reference genome (GRCh37/hg19) using BWA with standard parameters ³⁹, and filtered for high-quality (MAPQ ≥ 13), non-mitochondrial, and properly paired reads (longer than 18 nt). MACS2 (v 2.2.6) was used to call peaks using the parameters "-q 0.05 -g hs --nomodel --shift − 75 --extsize 150" ⁴⁰. Peaks with an FDR of lower than 5% were saved to detect chromosomal regions for further analyses. Bigwig files were generated from the filtered BAM files using deepTools2 bamCoverage with the options "--normalizeUsing RPKM" ⁴¹. Peak annotation was performed with HOMER annotatePeaks.pl ⁴². Heatmaps and average profile plots were generated using deepTools plotHeatmap. Coverage tracks were visualized using the trackViewer R package ⁴³.

CUT&Tag analysis

Cleavage under targets & tagmentation (CUT&Tag) was performed using the Hyperactive In-Situ ChIP Library Prep Kit for Illumina (TD901, Vazyme Biotech) following the manufacturer’s instructions. Briefly, cells were collected into a low absorbing tube, washed twice in 500 µL Wash Buffer, and incubated with concanavalin A-coated magnetic beads for 10 min at RT. The bead-bound cells were resuspended in 50 µL Antibody Buffer, followed by incubation with 1 µg of primary antibody at 4°C overnight with slow rotation. The supernatant was removed, and cells were incubated with secondary antibody in Dig-wash Buffer for 1 h at room temperature. Cells were washed three times with Dig-wash Buffer and incubated with 0.04 µM Hyperactive pG-Tn5 Transposase adapter mix prepared in Dig-300 buffer. Cells were washed three times with Dig-300 buffer to remove unbound pG-Tn5 and resuspended in 300 µL Tagmentation Buffer to incubate for 1 h at 37°C.The reaction was terminated, and DNA was extracted using phenol-chloroform-isoamyl alcohol method. PCR was performed to amplify the libraries under the following cycling conditions: 72°C for 5 min, 98°C for 30 s, 20 cycles of 98°C for 10 s, and 60°C for 30 s, followed by a final extension at 72°C for 1 min and holding at 4°C. PCR products were then purified with DNA Clean Beads. After quantification and quality control, DNA libraries were sequenced on an Illumina NovaSeq platform. The following antibodies were used: anti-rabbit IgG (#2729, Cell Signaling Technology), YY1 (ab109237, Abcam). Peaks were called using SEACR (v1.1) and MACS2 (v2.2.6) ^{40, 44}. The subsequent analyses were same as the ATAC-seq pipeline.

In vivo studies

To evaluate the function of YY1 in vivo, 1 × 10⁶ PC9 cells with YY1 genetic editing were mixed with Matrigel (BD Biosciences) and subcutaneously implanted in the dorsal flank of BALB/c nude mice (5 weeks of age). When tumor sizes reached approximately 200–300 mm³, animals were randomized into vehicle and treatment groups (8 mice each). For treatment of tumor-bearing mice, erlotinib was given at a dose of 5 mg/kg/day by gavage. Tumor volumes were recorded by blind measurement with a caliper and calculated as length × width² × 0.52. The institutional animal care and use committee of Ren Ji Hospital approved all animal protocols.

Statistical analysis

The sequencing data were deposited in the NCBI BioProject database under the accession number PRJNA973187. The TCGA data were downloaded from the UCSC Xena Explorer and processed as previously described ⁴⁵. The Kaplan-Meier method with log-rank test was used for survival analysis. The pathway enrichment analysis of differentially expressed genes was performed using the clusterProfiler R package (v3.18.0) and Metascape (http://metascape.org) ⁴⁶. Gene set enrichment analysis was performed using the GSEA software (v4.1.0). In all experiments, comparisons between two groups were based on two-sided Student’s t tests. Pearson’s correlation coefficient was used to measure the linear correlation between two variables. Graphics and statistics were generated using GraphPad Prism (v8.0) or R (v4.1.0). P values of < 0.05 were considered statistically significant.

Acknowledgements

This work was supported by the National Natural Science Foundation of China (82172596 to G Zhuang; 82273273 to Y Fu; 82072560 to X Zhao), Shanghai Municipal Health Commission (202140049 to MC Cai), Shanghai Science and Technology Plan Project Shanghai Sailing Program (23YF1441100 to H Lu), Science and Technology Commission of Shanghai Municipality (23ZR1438800 to P Ma; 21Y11913900 to Y Fu), Shanghai Municipal Education Commission-Gaofeng Clinical Medicine Grant Support (20161313 to G Zhuang), Collaborative Innovation Center for Clinical and Translational Science by Ministry of Education & Shanghai (CCTS-2022203 to G Zhuang), innovative research team of high-level local universities in Shanghai (SHSMU-ZLCX20210200 to G Zhuang), and 111project (no. B21024 to G Zhuang).

Sabnis AJ, Bivona TG. Principles of Resistance to Targeted Cancer Therapy: Lessons from Basic and Translational Cancer Biology. Trends Mol Med 2019, 25(3): 185–197.
Shen S, Vagner S, Robert C. Persistent Cancer Cells: The Deadly Survivors. Cell 2020, 183(4): 860–874.
Dhanyamraju PK, Schell TD, Amin S, Robertson GP. Drug-Tolerant Persister Cells in Cancer Therapy Resistance. Cancer Res 2022, 82(14): 2503–2514.
Hata AN, Niederst MJ, Archibald HL, Gomez-Caraballo M, Siddiqui FM, Mulvey HE, et al. Tumor cells can follow distinct evolutionary paths to become resistant to epidermal growth factor receptor inhibition. Nat Med 2016, 22(3): 262–269.
Ramirez M, Rajaram S, Steininger RJ, Osipchuk D, Roth MA, Morinishi LS, et al. Diverse drug-resistance mechanisms can emerge from drug-tolerant cancer persister cells. Nat Commun 2016, 7: 10690.
Sharma SV, Lee DY, Li B, Quinlan MP, Takahashi F, Maheswaran S, et al. A chromatin-mediated reversible drug-tolerant state in cancer cell subpopulations. Cell 2010, 141(1): 69–80.
Knoechel B, Roderick JE, Williamson KE, Zhu J, Lohr JG, Cotton MJ, et al. An epigenetic mechanism of resistance to targeted therapy in T cell acute lymphoblastic leukemia. Nat Genet 2014, 46(4): 364–370.
Rambow F, Rogiers A, Marin-Bejar O, Aibar S, Femel J, Dewaele M, et al. Toward Minimal Residual Disease-Directed Therapy in Melanoma. Cell 2018, 174(4): 843–855 e819.
Rusan M, Li K, Li Y, Christensen CL, Abraham BJ, Kwiatkowski N, et al. Suppression of Adaptive Responses to Targeted Cancer Therapy by Transcriptional Repression. Cancer Discov 2018, 8(1): 59–73.
Muranen T, Selfors LM, Worster DT, Iwanicki MP, Song L, Morales FC, et al. Inhibition of PI3K/mTOR leads to adaptive resistance in matrix-attached cancer cells. Cancer Cell 2012, 21(2): 227–239.
Song KA, Hosono Y, Turner C, Jacob S, Lochmann TL, Murakami Y, et al. Increased Synthesis of MCL-1 Protein Underlies Initial Survival of EGFR-Mutant Lung Cancer to EGFR Inhibitors and Provides a Novel Drug Target. Clin Cancer Res 2018, 24(22): 5658–5672.
Koppikar P, Bhagwat N, Kilpivaara O, Manshouri T, Adli M, Hricik T, et al. Heterodimeric JAK-STAT activation as a mechanism of persistence to JAK2 inhibitor therapy. Nature 2012, 489(7414): 155–159.
Shi K, Lu H, Zhang Z, Fu Y, Wu J, Zhou S, et al. Transient targeting of BIM-dependent adaptive MCL1 preservation enhances tumor response to molecular therapeutics in non-small cell lung cancer. Cell Death Differ 2022.
Herranz D, Ambesi-Impiombato A, Sudderth J, Sanchez-Martin M, Belver L, Tosello V, et al. Metabolic reprogramming induces resistance to anti-NOTCH1 therapies in T cell acute lymphoblastic leukemia. Nat Med 2015, 21(10): 1182–1189.
Bosc C, Saland E, Bousard A, Gadaud N, Sabatier M, Cognet G, et al. Mitochondrial inhibitors circumvent adaptive resistance to venetoclax and cytarabine combination therapy in acute myeloid leukemia. Nat Cancer 2021, 2(11): 1204–1223.
Konieczkowski DJ, Johannessen CM, Garraway LA. A Convergence-Based Framework for Cancer Drug Resistance. Cancer Cell 2018, 33(5): 801–815.
Marine JC, Dawson SJ, Dawson MA. Non-genetic mechanisms of therapeutic resistance in cancer. Nat Rev Cancer 2020, 20(12): 743–756.
Boumahdi S, de Sauvage FJ. The great escape: tumour cell plasticity in resistance to targeted therapy. Nat Rev Drug Discov 2020, 19(1): 39–56.
Wang Z, Hausmann S, Lyu R, Li TM, Lofgren SM, Flores NM, et al. SETD5-Coordinated Chromatin Reprogramming Regulates Adaptive Resistance to Targeted Pancreatic Cancer Therapy. Cancer Cell 2020, 37(6): 834–849 e813.
Asangani IA, Dommeti VL, Wang X, Malik R, Cieslik M, Yang R, et al. Therapeutic targeting of BET bromodomain proteins in castration-resistant prostate cancer. Nature 2014, 510(7504): 278–282.
Magnani L, Stoeck A, Zhang X, Lanczky A, Mirabella AC, Wang TL, et al. Genome-wide reprogramming of the chromatin landscape underlies endocrine therapy resistance in breast cancer. Proc Natl Acad Sci U S A 2013, 110(16): E1490-1499.
Guler GD, Tindell CA, Pitti R, Wilson C, Nichols K, KaiWai Cheung T, et al. Repression of Stress-Induced LINE-1 Expression Protects Cancer Cell Subpopulations from Lethal Drug Exposure. Cancer Cell 2017, 32(2): 221–237 e213.
Hinohara K, Wu HJ, Vigneau S, McDonald TO, Igarashi KJ, Yamamoto KN, et al. KDM5 Histone Demethylase Activity Links Cellular Transcriptomic Heterogeneity to Therapeutic Resistance. Cancer Cell 2018, 34(6): 939–953 e939.
Shi Y, Seto E, Chang LS, Shenk T. Transcriptional repression by YY1, a human GLI-Kruppel-related protein, and relief of repression by adenovirus E1A protein. Cell 1991, 67(2): 377–388.
Tang C, Livingston MJ, Liu Z, Dong Z. Autophagy in kidney homeostasis and disease. Nat Rev Nephrol 2020, 16(9): 489–508.
Levine B, Kroemer G. Biological Functions of Autophagy Genes: A Disease Perspective. Cell 2019, 176(1–2): 11–42.
Hughes D, Andersson DI. Evolutionary consequences of drug resistance: shared principles across diverse targets and organisms. Nat Rev Genet 2015, 16(8): 459–471.
Meliala ITS, Hosea R, Kasim V, Wu S. The biological implications of Yin Yang 1 in the hallmarks of cancer. Theranostics 2020, 10(9): 4183–4200.
Gordon S, Akopyan G, Garban H, Bonavida B. Transcription factor YY1: structure, function, and therapeutic implications in cancer biology. Oncogene 2006, 25(8): 1125–1142.
Zhang Q, Stovall DB, Inoue K, Sui G. The oncogenic role of Yin Yang 1. Crit Rev Oncog 2011, 16(3–4): 163–197.
Verheul TCJ, van Hijfte L, Perenthaler E, Barakat TS. The Why of YY1: Mechanisms of Transcriptional Regulation by Yin Yang 1. Front Cell Dev Biol 2020, 8: 592164.
Weintraub AS, Li CH, Zamudio AV, Sigova AA, Hannett NM, Day DS, et al. YY1 Is a Structural Regulator of Enhancer-Promoter Loops. Cell 2017, 171(7): 1573–1588 e1528.
Wang W, Qiao S, Li G, Cheng J, Yang C, Zhong C, et al. A histidine cluster determines YY1-compartmentalized coactivators and chromatin elements in phase-separated enhancer clusters. Nucleic Acids Res 2022, 50(9): 4917–4937.
Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nature methods 2012, 9(4): 357–359.
Kim D, Paggi JM, Park C, Bennett C, Salzberg SL. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nature biotechnology 2019, 37(8): 907–915.
Liao Y, Smyth GK, Shi W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics (Oxford, England) 2014, 30(7): 923–930.
Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 2014, 15(12): 550.
Buenrostro JD, Wu B, Chang HY, Greenleaf WJ. ATAC-seq: A Method for Assaying Chromatin Accessibility Genome-Wide. Curr Protoc Mol Biol 2015, 109: 21 29 21–21 29 29.
Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 2009, 25(14): 1754–1760.
Feng J, Liu T, Qin B, Zhang Y, Liu XS. Identifying ChIP-seq enrichment using MACS. Nat Protoc 2012, 7(9): 1728–1740.
Ramírez F, Ryan DP, Grüning B, Bhardwaj V, Kilpert F, Richter AS, et al. deepTools2: a next generation web server for deep-sequencing data analysis. Nucleic acids research 2016, 44(W1): W160-165.
Heinz S, Benner C, Spann N, Bertolino E, Lin YC, Laslo P, et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Molecular cell 2010, 38(4): 576–589.
Ou J, Zhu LJ. trackViewer: a Bioconductor package for interactive and integrative visualization of multi-omics data. Nat Methods 2019, 16(6): 453–454.
Meers MP, Tenenbaum D, Henikoff S. Peak calling by Sparse Enrichment Analysis for CUT&RUN chromatin profiling. Epigenetics & chromatin 2019, 12(1): 42.
Cai MC, Chen M, Ma P, Wu J, Lu H, Zhang S, et al. Clinicopathological, microenvironmental and genetic determinants of molecular subtypes in KEAP1/NRF2-mutant lung cancer. Int J Cancer 2019, 144(4): 788–801.
Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 2012, 16(5): 284–287.

(Not answered)

Download PDF

Journal Publication

published 27 Nov, 2024

Read the published version in Cell Death & Disease →

Editorial decision: revise
25 Jul, 2024
Review #2 received at journal
22 Jul, 2024
Reviewer #2 agreed at journal
14 Jul, 2024
Review #1 received at journal
14 Jul, 2024
Reviewer #1 agreed at journal
12 Jul, 2024
Reviewers invited by journal
12 Jul, 2024
Submission checks completed at journal
18 Jun, 2024
Editor assigned by journal
17 Jun, 2024
First submitted to journal
17 Jun, 2024

You are reading this latest preprint version

YY1 downregulation underlies therapeutic response to molecular targeted agents

Status:

Journal Publication

Version 1

Abstract

Figures

Background

Results

Characterization of chromatin accessibility dynamics implicates YY1 in EGFR inhibitor response

EGFR regulates YY1 expression through the MAPK signaling pathway

RTK/MAPK inhibition downregulates YY1 in diverse oncogene-addicted cancer models

YY1 depletion underlies therapeutic effects of molecular targeted agents

YY1 dictates cell cycle and autophagic programs

YY1 exhibits anticipated fluctuation following targeted treatment in lung cancer patients

Discussion

Materials and Methods

Cell culture and reagents

Plasmids and sgRNAs

Virus production and cell infection

Western blotting analysis

Immunofluorescence staining

EdU incorporation assay

Patient samples

Immunohistochemistry staining

Cell cycle analysis

Transmission electron microscopy

RNA extraction and quantitative PCR assay

RNA sequencing and analysis

ATAC sequencing

CUT&Tag analysis

In vivo studies

Statistical analysis

Declarations

Acknowledgements

References

Additional Declarations

Supplementary Files

Status:

Journal Publication

Version 1