1. Introduction
Cancer cells reprogram their energy metabolism to produce the enormous amounts of biomass needed for sustained tumor growth [
1]. Otto Warburg noted a high glycolytic activity of tumor cells even in the presence of oxygen (aerobic glycolysis). Today, this characteristic metabolic phenotype (called the ‘Warburg effect’) is considered a hallmark of cancer cells and attributed to a special dependence of highly proliferating cells on biosynthetic pathways that use intermediates derived from glycolysis [
2,
3,
4]. At the molecular level, this is achieved by an increased uptake of glucose and the downstream inhibition of glycolysis at the level of pyruvate kinase and pyruvate dehydrogenase. These processes can be pharmacologically targeted with 2-deoxy-D-glucose (2DG) and dichloroacetate (DCA), respectively [
5]. 2DG is a glucose analog that is taken up by cells through glucose transporters and phosphorylated by hexokinase. It cannot be further metabolized in the glycolysis pathway and therefore is considered a glycolysis inhibitor. DCA, a structural analog of pyruvate, functions as an inhibitor of pyruvate dehydrogenase kinase 1 (PDK1) and thus increases pyruvate dehydrogenase activity and stimulates efflux from glycolysis into the tricarboxylic acid cycle [
6]. Although the impact of 2DG and DCA on cancer cell metabolism is far from being understood, both compounds are widely considered as anti-Warburg drugs that interfere with the central role of glycolysis in cancer cells [
5]. Studies using 2DG and DCA have provided deeper mechanistic insight into cancer cell metabolism and have led to the development of novel therapeutic strategies to exploit the altered metabolism [
7]. In clinical trials, 2DG and DCA showed dose-limiting toxicity preventing their clinical use [
8]. Some newer drugs are currently being evaluated in preclinical models or clinical trials [
1,
8], but it appears that many are still limited more by toxicities than by their ability to kill cancer cells [
7]. A better understanding of the metabolic dependencies in specific tumor tissues holds the key for defining the aspects of metabolism most limiting for tumor growth and finding a therapeutic window to exploit those vulnerabilities for better cancer treatment [
7].
Macroautophagy (hereafter referred to as autophagy) is a physiological and evolutionary conserved self-digestion process in which intracellular components are sequestered in double-membrane vesicles called autophagosomes that fuse with lysosomes for the recycling of nutrients [
9,
10,
11]. Its progression involves the core autophagy machinery, encoded by autophagy-related genes (ATGs), and can be subdivided into the sequential steps: induction, nucleation, elongation, docking and fusion, degradation, and recycling [
12]. Induction is negatively controlled by the mammalian target of rapamycin (mTOR) and positively by the 5′ adenosine monophosphate-activated protein kinase (AMPK). Both protein kinases phosphorylate, with opposite effects, the ULK1/2 kinase, that forms the pre-initiation complex together with FIP200, ATG13, ATG17, and ATG101 [
13]. This, in turn, activates the Beclin-1 (also known as BECN1 or ATG6) initiation complex, which includes ATG4L, VPS34, UVRAG, and Bif-1. The Beclin-1 complex is essential for the nucleation of the phagophore, the immature form of the autophagosome, while vesicle elongation involves the microtubule-associated proteins 1A/1B light chain 3B (MAP1LC3B, also known as ATG8F or LC3), a member of the ATG8 protein family [
14]. Newly synthesized pro-LC3 is processed by the ATG4 protease to LC3-I and subsequently conjugated to phosphatidylethanolamine (PE) in a multistep process driven by the E1-like enzyme ATG7 and the E2-like enzymes ATG3 and ATG10 [
15]. PE-conjugated (lipidated) LC3, commonly referred to as LC3-II, is anchored to the phagophore membrane and functions in the elongation and as a docking receptor for adaptor proteins, such as SQSTM1 (also known as p62), that deliver cargo for degradation [
15]. Mature autophagosomes, containing organelles and proteins, fuse with lysosomes. The content is degraded, and the products are recycled into metabolic and biosynthetic pathways.
The metabolism-protective role of autophagy implies that autophagy defects render tumor cells hypersensitive to metabolic perturbation and open a therapeutic window for metabolic drug treatment. In support of this concept, mTOR upregulation, which is commonly observed as a drug resistance mechanism in a broad range of tumor types [
16,
17], blocks the autophagy pathway at various steps [
18] and renders tumors cells hypersensitive to well-tolerated doses of metabolic inhibitors that have little to no effect on normal autophagy-competent cells [
19]. In principle, autophagy can also be inactivated genetically. While single-nucleotide variants or short insertion–deletion mutations (commonly referred to as ‘mutations’) in ATGs are remarkably rare in human cancer [
20], a haploinsufficiency network-based analysis has identified autophagy as a pathway that is frequently hit by somatic copy number alterations (SCNAs), leading more often to losses than gains [
21]. SCNAs in ATGs are usually not homozygous losses, but rather monoallelic deletions affecting multiple ATGs in parallel [
21]. As single-allele losses can reduce messenger RNA (mRNA) levels [
22], monoallelic deletions hitting multiple ATGs in parallel may limit autophagic flux under metabolic stress and cause a therapeutically relevant vulnerability to metabolic cancer drugs. In support of this hypothesis, the RNAi knockdown of
BECN1 or
LC3 reduces autophagy flux and sensitizes tumors to autophagy inhibitors such as chloroquine [
21]. However, formal proof that the accumulation of monoallelic deletions in the autophagy pathway sensitizes to metabolic cancer drugs is missing.
Here, we used CRISPR/Cas9 technology to engineer tumor cells with various deletions of the ATGs, which are most frequently hit by SCNAs:
BECN1,
MAP1LC3B/LC3, and
ATG10 [
21]. While homozygous deletions of every single ATG blocked autophagy and sensitized to inhibitors of aerobic glycolysis, nonhomozygous deletions—even when hitting all three ATGs in parallel—did not create a druggable metabolic vulnerability. Altogether, this illustrates that the autophagy pathway of tumor cells is much more robust and resistant to somatic copy number losses than previously anticipated.
2. Materials and Methods
2.1. Cell Culture
The NCI-H460 cell line was obtained from the American Tissue Collection Center (ATCC) and cultivated in a humidified atmosphere at 37 °C and 5% CO2 using RPMI medium (Gibco) supplemented with 10% fetal bovine serum (FBS, Sigma-Aldrich, St. Louis, MO, USA), 100 U mL
−1 penicillin, and 100 μg mL
−1 streptomycin (Life Technologies, Carlsbad, CA, USA). Metabolic drugs were obtained from Sigma-Aldrich and used at the following concentrations: dichloroacetate (DCA) 20–60 mM and 2-deoxy-D-glucose (2DG) 2.5–40 mM. Standard concentrations for clonogenic growth assays were 40 mM DCA and 10 mM 2DG. H460 cells with hetero- or homozygous
ATG7 inactivation by CRISPR-mediated insertion or frameshift mutations were described previously [
19].
2.2. CRISPR/Cas9 Gene Editing
Single-guide RNAs (sgRNAs) targeting genes of interest were designed and cloned into the pSpCas9(BB)-2A-Puro vector (pX459) V2.0 (Addgene #62988) using Golden Gate Cloning as described [
23]. The following sgRNAs were used: ATG7, AGA AAT AAT GGC GGC AGC TA; LC3sg1, GGG AAG CAC CGT GTT CAT CG; LC3sg5, AGT TGT GAC CTG CTA CAC AT; BECN1sg1, TCC CTG TAA CAA CCC GTA CG; BECN1sg5, GAT CAC ATC ACA TGG TGA CC; BECN1sgExon4A, ATT GAA ACT CCT CGC CAG GA; ATG10sg3, TCC ATC CGT AAG TTT TCA AG; and ATG10sg4, CAA GGA GCT CCT GTA GAC TG. Cells were transfected with a single or a pair of pX459 plasmids using Lipofectamine 2000 (Thermo Fisher Scientific, Waltham, MA, USA) following the manufacturer’s protocol and selected with puromycin (1 μg mL
−1). Cells were single-cell cloned and analyzed for the presence of gene deletions and mutations by PCR and sequencing.
2.3. PCR and Sequencing
Genomic DNA from cell clones was analyzed by PCR using the following primers: LC3_del_FW (1), CAG ACC TCA GTG CCT CGG TCG A; LC3_del_RV (2), AGA GAA CGC CGC AGA TCC AGG T; LC3_int_FW (3), TGG AGG AAG GAC TGG GTC TC; LC3_int_RV (4), GGC TGT CTG GTG ATT CCT GTA A; BECN1_del_FW (1), TCC ACG GCC TCA GGG ATG GAA G; BECN1_del_RV (2), GTG CAC CCC TGG GCA GTT TTC A; BECN1_int_FW (3), TTC ACC ACA TTG GCC GGA CTG C; BECN1_int_RV (4), AGG GCG GAT GTC ACC AAG CTC T; BECN1 ex4 FW (5), AGG GCA TTC TGT CCT CTG CCC C; BECN1 ex4 RV (6), GCC ATG CTG GTC TTC CAC AGG G; ATG10_del_FW (1), TAC AGC ACC TAG AGT CCC GT; ATG10_del_RV (2), GGA AGC AGG GAG AAA AAA TCC TC; ATG10_int_FW (3), ACT CCC TTT TCC TTG CCT CAT AG; ATG10_int_RV (4), CAC GCT GAA GTC TTG ATA CCC T; ATG7ex2_FW (3), AGT TGT GTT TCA AGG TAG CCT G; and ATG7ex2_RV (4), CCG TGA GGA TAA CAG AAG ATG ATG. For each gene, one primer pair 1–2 was designed to amplify a fragment of more than 10 kbp in size spanning most of the gene (
Figure 1a). Under standard PCR conditions, these primers could only amplify alleles containing a large deletion of the intervening sequence. A PCR product with these primers identified clones with at least one deleted allele (
Figure 1b,d). Another primer pair 3–4 was designed to amplify a small internal fragment that indicated the presence of a nondeleted allele (
Figure 1b,d). In the case of
BECN1 and
ATG10, which are present in three copies in H460 cells, clones with monoallelic (+/+/−) and biallelic (+/−/−) deletion were distinguished by sequencing the PCR product of primer pair 1–2, which spans the fusion point. The presence of two different fusion sequences identified clones with two deleted alleles. The
BECN1+/−/− clone was retransfected with a nuclease targeting exon 4 and recloned. PCR amplification of exon 4 (primers 5–6) and sequencing identified two
BECN1fs/−/− clones with frameshift-inducing base pair insertions, respectively (
Figure 1c). Sequencing of PCR amplicons was performed by LGC Genomics GmbH (Berlin, Germany).
2.4. Western Blotting
Cells were harvested and lysed with NP-40 Lysis Buffer (50 mM Tris-HCl, 150 mM NaCl, 5 mM ethylenediaminetetraacetic acid (EDTA), 2% NP-40, pH 8.0) and supplemented with protease inhibitor (complete ULTRA tablets EASYpack, Roche, Basel, Switzerland). Protein yield was quantified via Bradford assay (Bio-Rad, Hercules, CA, USA). A total of 35–45 μg of proteins was loaded on NuPAGE SDS Gels (Life Technologies) and wet transferred to PVDF membranes. Membranes were blocked in tris-buffered saline with polysorbate 20 (TBST; 5 mM Tris, 15 mM NaCl, 0.1% Tween 20, pH 7.5) with 5% nonfat dry milk. The primary antibodies used were: LC3B (LC3-I/II) (1:1000, ab48394, Abcam, Cambridge, UK), p62/SQSTM1 (1:1000, P0067, Sigma, St. Louis, MO, USA), ATG7 (D12B11) (1:1000, #8558, Cell Signaling, Danvers, MA, USA), BECN1 (E8) (1:500, sc-48341, Santa Cruz Biotechnology, Dallas, TX, USA), ATG10 (EPR4804) (1:1000, ab124711, Abcam), and β-Actin (AC-15) (1:10,000, ab6276, Abcam). The secondary antibodies used were: goat anti-mouse IgG-HRP (1:5000, #A16084, Thermo Fisher Scientific) and donkey anti-rabbit IgG-HRP (1:5000, #NA9340, GE Healthcare, Chicago, IL, USA). Detection: WesternBright ECL Substrate Sirius kit (Biozym, Hessisch Oldendorf, Germany).
2.5. Clonogenic Growth Assays
Cells were seeded on cell culture plates, treated after 24 h, and cultivated for up to 10 days. Cells were fixed in 70% ethanol overnight and stained with Crystal Violet (Sigma-Aldrich) solution diluted 1:20 in 20% ethanol. Colony counting was performed manually, taking into consideration only colonies with a cell number ≥ 50.
2.6. Real-Time Live Cell Imaging
Live cell imaging was performed by seeding cells in 96-well plates in triplicate for each condition. Drug treatment was started the next day. Cell proliferation was recorded for up to 7 days every 4 h by the IncuCyte S3 Live-Cell Analysis System (Sartorius, Göttingen, Germany) with a phase-contrast microscope at 10× magnification. ImageJ software was used to crop pictures and add scale bars. Confluence analysis was performed with IncuCyte S3 2018A software. Area under the curve (AUC) was calculated with GraphPad Prism 8 using the mean of the first two time points as baseline. AUC values were normalized to the untreated control.
2.7. TCGA Pan-Cancer Atlas Analysis
TCGA Pan-Cancer Atlas data were downloaded from the cBioPortal platform [
24]. In our study, we included the 9889 patient samples from 32 different cancer types for which gene copy numbers and mRNA expression data for
BECN1,
ATG7, and
SQSTM1 were available. Log2-transformed copy number data were from the Affymetrix SNP6 platform, and mRNA expression data were used as mRNA Z-scores relative to diploid samples (RNA Seq V2 RSEM). The correlation analysis between copy number and protein levels was restricted to 307 breast, colorectal, and ovarian cancer samples for which additional protein expression data measured by mass spectrometry were available from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) [
25]. All analyses and statistics were calculated with GraphPad Prism 9 Software.
2.8. Statistical Analysis
GraphPad Prism 9 Software was used to generate all plots and perform statistical analysis. All data are shown as mean ± SD with sample numbers indicated in figure legends. Individual data points are shown as dots. Experiments comparing multiple groups were analyzed by one-way analysis of variance (1-way ANOVA) followed by multiple comparison testing according to Dunnett. Experiments investigating the interaction of two variables (e.g., cell type and treatment) were analysed using two-way analysis of variance (2-way ANOVA) followed by multiple comparison testing according to Dunnett. p values are reported as follows: ***, p < 0.0001; **, p < 0.001; and *, p < 0.01.
4. Discussion
Autophagy plays a key role in maintaining metabolic homeostasis during tumor progression and cancer therapy [
11,
12]. This prosurvival activity makes autophagy an attractive target for cancer therapy, and various lines of evidence confirm that the inhibition of autophagy has the potential to eradicate even advanced tumors. For example, acute autophagy ablation in mice with pre-existing NSCLC blocked tumor growth, promoted tumor cell death, and generated more benign disease (oncocytomas) [
28]. While indicating that tumors are more dependent on autophagy than normal tissues, these studies also demonstrated that systemic autophagy deficiency in adult mice leads to gradual depletion of dedicated nutrient stores, which is further accelerated by fasting and can result in death from hypoglycemia [
28]. To avoid toxicities of systemic autophagy inhibition, an alternative therapy strategy would be to identify tumors with intrinsic autophagy defects that render the metabolism of such tumor cells more susceptible to perturbation and treat those tumors with metabolic inhibitors.
An autophagy defect can be caused by negative regulators of autophagy such as mTOR [
18,
29]. mTOR signaling is frequently upregulated in tumors with acquired cancer drug resistance as it, for example, protects from the cytotoxic DNA crosslinks induced by cisplatin by promoting DNA repair and counteracts the cytotoxic activity of PI3K-inhibitors (PI3Ki) by blocking apoptosis [
16,
17]. However, as collateral damage, mTOR blocks autophagy and thereby simultaneously sensitizes such cisplatin- or PI3Ki-resistant tumor cells to drugs that interfere with glycolysis, i.e., the Warburg effect [
19]. Importantly, drugs that target glycolysis also affect normal cellular metabolism and are therefore limited more by toxicities than by their ability to kill cancer cells [
7]. mTOR-mediated inhibition of autophagy, however, sensitized tumors cells to such an extent that tumors could be eradicated in mice using well-tolerated doses of glycolysis inhibitors, arguing that autophagy defects in tumor cells can open a therapeutic window for metabolic cancer treatments.
In principle, the autophagy pathway can also be compromised by genetic alterations. However, despite clear evidence that autophagy suppresses cancer initiation in mouse models, ATGs are not commonly inactivated by homozygous mutations or gene deletions [
20], likely because of their essential role for maintaining metabolic homeostasis during tumor progression and cancer therapy [
11,
12]. While homozygous deletions are therefore disfavored during tumor evolution, nonhomozygous deletions resulting from somatic copy number losses are conceivable and might, especially when affecting multiple ATGs in parallel, compromise autophagic capacity to an extent that is compatible with normal tumor progression but might create a vulnerability to metabolic perturbation. Indeed, the autophagy pathway is frequently affected by somatic copy number alterations (SCNAs), with losses being much more common than gains in a broad range of tumors [
21]. These include tumor entities that are particularly rich in SCNAs such as ovarian carcinomas, but also in tumors like non-small cell lung cancer (NSCLC), where SCNAs are less dominant drivers of tumorigenesis than point mutations [
21,
30]. In some tumors, nonhomozygous SCNAs indeed affect multiple ATGs, suggesting that the combined loss synergistically limits autophagic flux especially under autophagy-stressing conditions [
21]. In fact, the ovarian cancer cell line OVCAR-3 with monoallelic losses of
BECN1 and
LC3 is more sensitive to various autophagy-stressing drugs than the SKOV-3 ovarian cancer cell line without these deletions, while the latter is sensitized by the RNAi knockdown of either
BECN1 or
LC3 [
21]. Altogether, these results strongly suggested that monoallelic losses—especially when affecting multiple genes at once—confer a druggable vulnerability. We therefore hypothesized that ATG copy number losses might identify tumors that are also less capable of compensating inhibition of aerobic glycolysis through autophagy.
Although the NSCLC cells in our study were strongly sensitized to 2DG and DCA by the knockout of single ATGs (
Figure 2), we did not observe a similar metabolic vulnerability when we experimentally deleted only single copies with CRISPR nucleases (
Figure 3). Importantly, we did not even observe a comparable vulnerability when deleting three core autophagy genes down to a single remaining copy of each (
Figure 4 and
Figure 5). Notably, we observed only a modest reduction of protein levels and weak signs of decreased autophagic flux such as reduced LC3 conversion and p62 accumulation. In general, SCNAs are well-known to alter the expression level of genes located at the affected genomic region and thereby contribute to or even drive tumor development [
31,
32,
33]. There is an overall close correlation between gene copy number variation and differential gene expression across a broad range of cancer types, and the copy number displays a positive linear influence on gene expression for the majority of genes, indicating that genetic variation has a direct effect on gene transcription [
27]. Due to transcriptional adaptive mechanisms, however, changes in gene copy number at the genomic level do not always translate proportionally into altered gene expression levels [
34,
35,
36]. Although most of the autophagy genes do not show significant changes in mRNA levels when autophagy is induced, expression of these genes is nevertheless tightly regulated at the level of transcription and post-transcriptionally, for example, via miRNAs [
26,
37,
38]. The mechanisms underlying transcriptional and post-transcriptional adaptation of genes to SCNAs still remain poorly understood and appear to be pathway-dependent as some biological processes show higher degrees of adaptation than others [
34,
35,
39]. For the autophagy pathway, wide-spread adaption to SCNAs is supported by our analysis of the TCGA Pan-Cancer Atlas cohort (
Figure 6). Across all patients, the copy number of key autophagy genes
BECN1 and
ATG7 correlated well with mRNA expression but to a much lesser degree with protein levels (
Figure 6a–d). Moreover, although the p62 protein correlated with the
SQSTM1/p62 copy number, no accumulation of the p62 protein was detected in patient samples with shallow deletions, i.e., nonhomozygous copy number losses, in single or multiple autophagy genes (
Figure 6e–h).
This demonstrates that the extent of copy number reduction detected at the genome level is strongly decreased at the protein level, which blunts potential negative effects on autophagic flux that would impede tumor progression or sensitize it to cancer therapy. In the future, it will be important to examine how the effects of reduced copy number are blunted at the level of, for example, mRNA stability, translation, or protein stability. Inhibiting these transcriptional or post-transcriptional adaptation processes could serve as a novel strategy to interfere with autophagy and sensitize cancer cells to metabolic compounds and other cancer drugs, whose therapeutic efficacy is often limited by the induction of autophagy. Moreover, such strategies would selectively block autophagy in cancer cells with ATG copy number aberrations and thereby avoid toxic side effects resulting from the systemic inhibition of autophagy.