Improvement of ACK1-targeted therapy efficacy in lung adenocarcinoma using chloroquine or bafilomycin A1

RNA-sequencing

Details of the silencing of ACK1 in A549 cells by lentivirus-medicated shRNA and RNA-sequencing were provided elsewhere (Zhu et al. 2021a, b).

Bioinformatics analysisConstruction of a multiple gene signature with ACK1-associated autophagy genes

We retrieved 20,198 ACK1-related genes (P < 0.05) from the TCGA-LUAD cohort and 232 autophagy genes Human Autophagy Database (HADb, http://autophagy.lu/clustering/index.html). Totally, 149 autophagy genes overlapping ACK1-associated genes were defined as ACK1-related autophagy genes. We further screened for genes significantly associated with overall survival in the TCGA-LUAD cohort using the univariate Cox regression analysis. The significant genes were entered into the least absolute shrinkage and selection operator (LASSO) Cox regression algorithm to establish an optimal risk model with ACK1-related autophagy genes as published previously (Cao et al. 2021a, b; Liu et al. 2020, 2019).

Next, using a risk score formula linearizing the expression levels of the gene signature in LUAD patients, we quantitated the risk for unfavorable survival for each patient (Liu et al. 2019; Zhu et al. 2021a, b). Furthermore, we built a Cox-based nomogram to test the ability of the risk score to predict personal OS, with a concordance index (C-index) to measure its discriminative ability (Iasonos et al. 2008). Nomogram-predicated probability was compared with the observed outcome by plotting the calibration curve. Decision curve analysis (DCA) was used to assess the clinical net benefit of the risk score compared to ACK1 and TNM stage (Vickers et al. 2008; Vickers and Elkin 2006).

Evaluation of tumor immune environment and drug sensitivity

We first estimated the association of risk score with immune checkpoint genes or DNA mismatch repair genes with the R package ggstatsplot in the TCGA-LUAD cohort because they were considered as a predictive biomarker for immunotherapy (Chan et al. 2019; Le et al. 2017; Luchini et al. 2019; Rizvi et al. 2018). Based on the metagene methodology, we used the single-sample Gene Set Enrichment Analysis (ssGSEA) algorithm to calculate the fraction of 28 immune cell subpopulations in LUAD (Charoentong et al. 2017). We also downloaded the immunophenoscore (IPS) dataset for LUAD patients from The Cancer Immunome Atlas (TCIA, https://tcia.at/home). The IPS was derived by comprehensively integrating four crucial tumor immunogenicity determinants, that is, major histocompatibility complex (MHC) molecules (antigen processing), effector cells, immunosuppressive cells, as well as checkpoints and immunomodulators (Charoentong et al. 2017).

Moreover, to predict other drug sensitivity, an R package named pRRophetic was generated (Geeleher et al. 2014a, b) with reference to gene expression microarray data of near 700 cell lines before and after the administration of 138 drugs from the Cancer Genome Project (CGP). This R package (https://github.com/paulgeeleher/pRRophetic) permitted us to predict clinical drug sensitivity by analyzing gene expression profiles of tumors. By applying this methodology, we calculated half inhibitory concentrations (IC50) of standard anticancer drugs for both high- and low-risk groups.

Cell culture

We obtained human lung bronchial epithelial (BEAS-2B, cat# CBP60577) from COBIOER Biosciences CO., LTD (Nanjing, China) and a number of lung cancer cells from the Cell Bank of the Chinese Academy of Science (Shanghai, China), including A549 (cat# SCSP-503), PC-9 (cat# SCSP-5085), HCC827 (cat# SCSP-538), NCI-H460 (cat# SCSP-584), NCI-H1299 (cat# SCSP-589), NCI-H1915 (cat# SCSP-597), and H1650 (cat# SCSP-592). Cells were maintained in the recommended medium, DEME medium for Beas-2B, or RPMI 1640 for the rest of the cell lines (Procell, China) with the addition of 10% fetal bovine serum (Hyclone, Life Sciences, Shanghai, China), penicillin G (100 U/ml, Beyotime, China), streptomycin (100 μg/ml, Corning, China) in a humidified incubator with 5% CO2, at 37 °C.

Lentivirus infection

Lentivirus encoding shRNA targeting ACK1/TNK2 and a negative control shRNA were purchased from GeneChem (Shanghai, China). Sequence targeting ACK1 (RNAi, tgCTTCCT CTTCCACCCAATT) were inserted in pLVshRNA-puro. A pLVX-Puro vector carrying the coding DNA sequence (CDS) region of ACK1/TNK2 was obtained from the same company and used to overexpress ACK1 in the lung cancer cells. Lentivirus infection was conducted on cells while they reached 80% confluency, with a multiplicity of infection (MOI) of 50. The shACK1 cells, ACK1 overexpression cells, and respective control cells were passaged in a culture medium supplemented with puromycine to establish stable cell lines. Total RNA was extracted to detect ACK1 mRNA expression levels using TRIzol reagent (Invitrogen, Thermo Fisher Scientific, USA). ACK1 transcripts were amplified using real-time PCR with GAPDH as an internal control. The primer information was referred to a previous publication.

Plasmids, transfection, and RNA interference

Plasmid (hU6-MCS-Ubiquitin-EGFP-IRES-puromycin) overexpressing PRKAA1 shRNA and controls were obtained from Shanghai Genechem Co., Ltd. (China). We used the shRNA sequence CATAAAGTAGCTGTGAAGATA to knock down the PRKAA1 gene. While reaching 80% confluence, cells in 6-well plates were transfected with control plasmids or plasmid encoding shRNAs targeting PRKAA1 (50 nM) using jetPRIME® transfection reagent (Polyplus Transfection Inc. New York, NY, USA). Cells were maintained in a culture medium for 24 h before being used for experiments.

Materials and reagents

AIM-100, chloroquine (CQ), and bafilomycin A1 (BafA1) were purchased from MedChemExpress (MCE, Princeton, NJ, USA). Antibodies purchased from Cell Signaling Technology (Danvers, MA, USA) were as follows: anti-ACK1, anti-total AMPK and anti-phosphorylated-AMPKα1 (Ser485), anti-total and anti-phosphorylated-mTOR (Ser2448), anti-Agt5, anti-beclin 1, anti-LC3, and anti-p62 antibodies. Antibody against phosphorylated-ACK1 (Y284) was obtained from Abcam Inc. (Cambridge, MA, USA).

Apoptosis analysis

Apoptosis was detected with an Annexin-V APC detection kit (eBioscience, USA). Briefly, cells subjected to different treatments were harvested and incubated with anti-Annexin V antibody labeled with APC and PI for 10 min in the dark, following the protocol provided by the manufacturer. Apoptotic cells were quantified using FACS Calibur flow cytometry.

Western blot analysis

Cells were grown in the 75 mm flask and treated with different concentrations of drugs. We collected and disintegrated cells in RIPA buffer at indicated times. Whole-cell extracts were prepared and separated in SDS-PAGE and blotted onto a polyvinylidene difluoride membrane. Blots were visualized using Immobilon Western Chemiluminescence HRP substrate (Millipore, Billerica, MA). If needed, blots were washed off using a stripping buffer, followed by reprobation with different primary antibodies.

Cell viability assay

The cancer cell suspension was added to 96-well plates at 1000 to 5000 cells/wells. After attachment, cells were treated with various concentrations of AIM-100 for 72 h. Cell viability was determined using Dojindo cell counting kit-8 (CCK-8, GlpBio, USA) at 24, 48, and 72 h, following the manufacturer’s instructions.

Colony formation assay

Cells were plated in a 12-well plate at a density of 800 cells per well. Cells grew for ten days in a 37 °C incubator until small cell colonies were observed with the naked eye. Then, cells were fixed with 4% paraformaldehyde for 20 min, followed by staining with 0.2% crystal violet at room temperature. Image J was used to quantify the relative density of colonies with different treatments.

Wound healing assay

A549 cells were cultured in the 6-well plates. While cells reached 95% confluence, vertical scratches were created on monolayers. And afterward, a serum-free medium was used to maintain cells. Images were taken, and gaps in the wounds were measured at 0 h and 18 h.

Migration assay

Migration assays were carried out using Transwell chambers (8 µm; Corning, Tewksbury, MA, USA). Briefly, cells were harvested, washed, and resuspended. Cell suspension with serum-free DMEM was added to the upper wells of the chambers at a density of 5 × 104 cells/well, whereas the lower wells of the chambers contained DMEM supplemented with 10% FBS serving as a chemoattractant. The transwell chambers were maintained in a 37 °C incubator for 18 h to allow cells to migrate to the lower surface of the filter. Migrated cells were fixed with 4% paraformaldehyde, visualized with 0.1% crystal violet, and numerated under a microscope.

Monitoring autophagosome formation

Various methods have been developed to measure autophagy, including the long-lived protein degradation assay, the lactate dehydrogenase sequestration assay, and the mRFP-GFP-LC3B fusion protein assay (Klionsky et al. 2021, 2016; Luhr et al. 2018a, b; Luhr et al. 2018a, b). Adenoviral vectors expressing mRFP-GFP-LC3 fusion protein and empty vectors were obtained from HanBio (Shanghai, China). A549 cells infected with adenoviral vectors were cultured for 24 h to allow the expression of mRFP-GFP-LC3B fusion protein. And then, cells were treated with AIM-100 (20 μm) or Dasatinib (20 μm) for an additional 12. Cells treated with DMSO served as control. After fixation with 4% paraformaldehyde (PFA) for 15 min, images of cells were taken using NIKON-TS2 fluorescence microscopy (Nikon Instruments Inc., Japan) with NIS-Elements F imaging software). The mRFP-GFP-LC3 fusion proteins were diffused in the cytoplasm of cells; therefore, both fluorophores fluoresce were very weak. Upon the stimulation, mRFP-GFP-LC3 fusion proteins were recruited to the membrane of autophagosomes. As a result, autophagosomes could be visualized as yellow fluoresce puncta. When an autophagosome fused with a lysosome, only red puncta can be observed because GFP signals were quenched in the lower pH environment of the autolysosome.

In vivo tumorigenesis study

The Institutional Review Board of Harbin Medical University Cancer Hospital approved the animal study protocol. Female BALB/c nude mice at 4–5 weeks of age were purchased from Charles River (Beijing Vital River Laboratory Animal Technology Co., Ltd., China). Mice were kept under specific pathogen-free conditions.

Every mouse received a dose of 1 × 106 A549 cells through subcutaneous injection. Mice were randomized into four groups ten days post-injection: Group 1: 0.1% DMSO as vehicle control; Group 2: CQ (30 mg/kg) via intraperitoneal injection every other day; Group 3: Dasatinib (30 mg/kg) administrated intragastrically; Group 4: the combination of CQ (30 mg/kg) and Dasatinib (20 mg/kg). The dosages of ACK1 inhibitor, Dasatinib (20 mg/kg), and autophagy inhibitor, CQ, were determined based on previous publications (Wang et al. 2018; Zhang et al. 2020), and both drugs were administrated every other day for four weeks. Tumor sizes were measured and recorded regularly. Mice were euthanized, and tumors were harvested and weighed at the end.

Statistics

The statistical analysis was performed using SPSS version 22 (SPSS, Inc., Chicago, IL, USA) and R version 4.0.3 (https://www.r-project.org/). We integrated the differences between two groups using the Student’s t-test, and the differences among three groups or more were tested with one-way ANOVA. If a significant result was found in the latter test, Tukey’s multiple comparisons tests were performed to determine which two groups the significant difference existed. Kaplan–Meier survival curves of OS were plotted for high- and low-risk groups. Receiver operating characteristic (ROC) curves were used to determine the prognostic accuracy of risk factors. Both univariate and multivariate Cox proportional hazards regression analyses were conducted to assess the association of the combined score and clinicopathological characteristics with overall survival. P < 0.05 was considered to be significant.

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