Starving cancer from the outside and inside: separate and combined effects of calorie restriction and autophagy inhibition on Ras-driven tumors

Laura M Lashinger
†
Ciara H O'flanagan
†
Sarah M Dunlap
Audrey J Rasmussen
Shannon Sweeney
Jessie Yangxiang Guo
Alessia Lodi
Stefano Tiziani
Eileen White
Stephen D Hursting
Starving cancer from the outside and inside: separate and combined effects of calorie restriction and autophagy inhibition on Ras-driven tumors
10.1186/s40170-016-0158-4R E S E A R C H Open Access
Background: Calorie restriction (CR) prevents obesity and exerts anticancer effects in many preclinical models. CR is also increasingly being used in cancer patients as a sensitizing strategy prior to chemotherapy regimens. While the beneficial effects of CR are widely accepted, the mechanisms through which CR affects tumor growth are incompletely understood. In many cell types, CR and other nutrient stressors can induce autophagy, which provides energy and metabolic substrates critical for cancer cell survival. We hypothesized that limiting extracellular and intracellular substrate availability by combining CR with autophagy inhibition would reduce tumor growth more effectively than either treatment alone. Results: A 30 % CR diet, relative to control diet, in nude mice resulted in significant decreases in body fat, blood glucose, and serum insulin, insulin-like growth factor-1, and leptin levels concurrent with increased adiponectin levels. In a xenograft model in nude mice involving H-Ras G12V -transformed immortal baby mouse kidney epithelial cells with (Atg5 +/+ ) and without (Atg5 −/− ) autophagic capacity, the CR diet (relative to control diet) genetically induced autophagy inhibition and their combination, each reduced tumor development and growth. Final tumor volume was greatest for Atg5 +/+ tumors in control-fed mice, intermediate for Atg5 +/+ tumors in CR-fed mice and Atg5 −/− tumors in control-fed mice, and lowest for Atg5 −/− tumors in CR mice. In Atg5 +/+ tumors, autophagic flux was increased in CR-fed relative to control-fed mice, suggesting that the prosurvival effects of autophagy induction may mitigate the tumor suppressive effects of CR. Metabolomic analyses of CR-fed, relative to control-fed, nude mice showed significant decreases in circulating glucose and amino acids and significant increases in ketones, indicating CR induced negative energy balance. Combining glucose deprivation with autophagy deficiency in Atg5 −/− cells resulted in significantly reduced in vitro colony formation relative to glucose deprivation or autophagy deficiency alone.

Background

Calorie restriction (CR), a nutritionally replete dietary regimen that reduces calorie intake without incurring malnutrition, extends lifespan in species from yeast to mammals, and in rodents delays the onset of multiple age-associated diseases, including cancer [1][2][3][4][5][6][7][8]. CR improves health and survival in rhesus monkeys, including decreased risks of diabetes, neurodegeneration, and cancer [9][10][11][12]. Though it is not yet known whether CR can extend lifespan in humans, CR improves markers of cardiovascular aging and skeletal decline, and decreases inflammatory and endocrine markers typically associated with age-related diseases such as cancer [13][14][15][16][17][18], suggesting that the beneficial effects of CR on metabolism and chronic disease risk in rodent and primate models may also extend to humans. In addition, CR can reprogram several cancer-related signaling pathways and increase the susceptibility of some cancer cells to chemotherapeutic agents [19,20].

CR redirects metabolic investment from cellular replication toward maintenance and survival [19,20]. Many of the beneficial effects of CR may be attributed to signaling through the AMP-activated protein kinase/ mammalian target of rapamycin (AMPK/mTOR) axis [19,20]. This system integrates intracellular and extracellular signals and enables cells to adapt to environmental changes, such as nutrient or growth factor availability. Activation of mTOR promotes cell survival, proliferation, and protein translation pathways. In low nutrient conditions, AMPK acts as a molecular sensor activating fatty acid oxidation and glucose uptake, inhibiting mTOR signaling, and regulating synthesis of proteins, lipids, glycogen, and nucleotides. Many cancers can hijack mTOR/AMPK signaling to promote their growth in changing external circumstances [21]. CR suppresses mTOR and increases AMPK activity in many tissues and organs [22]. A key target of the AMPK/mTOR pathway is macroautophagy (hereafter referred to as autophagy), an evolutionarily conserved, tightly controlled pathway in which proteins, macronutrients, and organelles are sequestered in double-membraned vesicles and lysosomally degraded. Autophagy is a homeostatic process, providing a conduit for clearance of damaged organelles and aggregated proteins. Autophagy can also function to promote cell survival during times of nutrient deprivation, as digested molecules can be recycled to provide energy and metabolic precursors until external nutrients are replenished [23]. Autophagy is inhibited by mTOR activation, while AMPK induces autophagy during low nutrient availability [24].

The role of autophagy in cancer is complex and likely context-dependent. Autophagy can be tumor suppressive, in particular during tumor initiation [25], and is regulated by several oncogenes including Myc, Akt, PI3-kinase, and Ras [26]. Oncogenic mutations in Ras are known to induce autophagy, and many Rasdriven tumors are dependent on autophagy [27][28][29], likely to provide an ancillary source of energy to rapidly proliferating cancer cells. In established tumors, autophagy induction can be prosurvival, allowing cancer cells to overcome nutrient deprivation, hypoxic conditions, oxidative and metabolic stress, and even chemotherapy [27,30,31].

The anti-aging effects of CR in Caenorhabditis elegans are dependent on autophagy [32], though the interaction between CR and autophagy in mammals is less clear [33], and manipulation of autophagy in combination with CR on tumor development has not been previously investigated. We hypothesized that limiting extracellular and intracellular substrate availability by combining CR with autophagy inhibition would reduce in vivo tumor growth more effectively than either treatment alone. Using an H-Ras-transformed xenograft tumor model, we show here that CR (in association with a metabolic shift away from glucose utilization and toward fatty acid oxidation) and autophagy deficiency each suppresses tumor growth. Moreover, combining CR with autophagy deficiency further reduces tumor growth relative to either intervention alone.

Methods

Cell lines

Immortalized baby mouse kidney epithelial cells (iBMK) were derived from Atg5 +/+ or Atg5 −/− mice and maintained as described previously [28,33,34]. Briefly, iBMK cells were immortalized by E1A and dominant-negative p53 expression. iBMK cells were then stably transfected with a H-Ras G12V expression construct, and a p-tFl-RFPtandem tagged microtubule-associated protein1 light chain 3 (LC3) expression plasmid [28]. Cells were cultured in DMEM (GIBCO/Invitrogen) supplemented with 10 % fetal bovine serum (FBS), and 1 % penicillin/ streptomycin (Sigma-Aldrich) at 38.5°C with 8.5 % CO 2 . For colony formation assay, 500 cells were seeded in triplicate into each well of a six-well plate and incubated for 10 days in DMEM containing 1, 5, or 10 mM glucose. Colonies were fixed for 5 min with 100 % methanol and stained with 0.5 % crystal violet in 50 % methanol.

Mice, diets, and experimental design

All mouse experimentation was conducted in the Animal Resource Center at the University of Texas at Austin in accordance with the recommendations outlined in the Guide for the Care and Use of Laboratory Animals set forth by the National Institutes of Health. All experimentation was approved by the Institutional Animal Care and Usage Committee at the University of Texas at Austin. All efforts were taken to minimize animal suffering.

Female athymic nude mice (4-6 weeks of age; n = 94) were singly housed and randomly selected to receive one of the following diets (n = 47/diet group): (1) control diet consumed ad libitum (#D12450B, a modified AIN-76A semipurified diet with 10 kcal% fat) that generates an overweight phenotype or (2) 30 % calorie-restricted diet (CR, #D0302702) consumed in daily aliquots that generate a lean phenotype. The CR diet is based on the control diet but modified such that when fed as a daily aliquot provides 70 % of the total caloric intake but 100 % of all vitamins, minerals, fatty acids, and amino acids. Both diets were from Research Diets (New Brunswick, NJ).

Following 16 weeks on either control or CR diet, a subset of mice (n = 19/diet group) were fasted for 6 h, anesthetized with CO 2 , and whole blood was collected by cardiac puncture for fasting glucose assessment and to obtain serum samples for circulating hormone and growth factor analysis. Mice were subsequently killed by cervical dislocation. Blood was allowed to coagulate (30 min at room temperature), centrifuged at 9300×g for 5 min, and serum was collected and stored at −80°C. Carcasses were analyzed for body composition parameters using dual energy X-ray absorptiometry (GE Lunar PIXImus; Fitchburg, WI).

The remaining 28 mice for each diet group were randomly selected for subcutaneous injection (left flank) with 5 × 10 4 autophagy-competent Atg5 +/+ ;Hras V12 iBMK (Atg5 +/+ ) cells (n = 14 mice) or autophagydeficient Atg5 −/− ;Hras V12 iBMK (Atg5 −/− ) cells (n = 14 mice). The respective diet regimens were continued for the duration of the study, and tumor growth was monitored for 4 weeks. Tumors were palpated and measured with Vermeer™ calipers weekly, and tumor volume was calculated using the formula 4/3п (r 1 ) 2 (r 2 ). Mice were fasted for 6 h, anesthetized with CO 2 for cardiac puncture (to obtain serum for metabolomics analysis), and then subsequently killed by cervical dislocation. Tumors were harvested, weighed, and either snap frozen in liquid nitrogen and stored at −80°C or embedded in optimal cutting temperature compound (OCT) and stored at −20°C.

Blood glucose and serum energy balance-related hormone analyses

Analysis of fasting glucose was performed on blood collected at the 16-week time point using a Contour glucometer and glucose test strips (Bayer HealthCare LLC, Mishawaka, IN). Serum insulin-like growth factor (IGF)-1 levels were determined by ELISA (Quantikine IGF-1 ELISA kit; R&D Systems, Inc., Minneapolis, MN), according to manufacturer's directions. Serum levels of insulin, leptin, and adiponectin were measured using Lincoplex® bead-based assays (Millipore Corporation, Billerica, MA) on a BioRad Bioplex® analyzer (BioRad, Hercules, CA), according to manufacturer's directions.

Metabolomic sample preparation and magnetic resonance spectroscopy acquisition

Frozen serum samples from a random sample of 11 mice/diet group that were injected with Atg5 +/+ tumor cells at 16 weeks of study and terminated at 20 weeks of study were deproteinized by ultrafiltration (Nanosep 3K OMEGA, Pall Corporation, MI) at 4°C [35,36]. Filtered serum was placed in 3 mm magnetic resonance spectroscopy (MRS) tubes (Norell, Landisville, NJ) containing phosphate buffer (final concentration 100 mM, pH 7.0), sodium 3-(trimethylsilyl)propionate-2,2,3,3-d4 (TMSP, 0.1 mM final concentration; Cambridge Isotope Laboratories, Andover MA), 0.75 % (w/v) sodium azide and 10 % D 2 O (final concentration; Cambridge Isotope Laboratories).

One-dimensional 1 H-MRS spectra were acquired on the filtered serum samples using a 700-MHz Bruker Avance spectrometer equipped with a Bruker Triple Resonance TCI cryoprobe and SampleXpress highthroughput robotics (Bruker BioSpin Corp., MA). Each sample was allowed to equilibrate for 5 min inside the probe before starting data acquisition. The acquisition parameters for 1D spectra were a 90°flip angle, 6-kHz spectral width, 1-s relaxation delay, 32,000 data points, 8 dummy scans, and 6-kHz spectral width. Excitation sculpting pulse sequence was implemented to suppress the water signal [37].

Histopathologic and immunohistochemical analyses

Tumors were fixed in formalin, embedded in paraffin, cut into 4-μm thick sections, placed on glass slides (one section/slide), and processed for either hematoxylin and eosin (H&E) or immunohistochemical (IHC) staining. Slides were deparaffinized in xylene and hydrated, and antigens were retrieved by microwaving slides for 10 min with 10 mM citrate buffer. Endogenous peroxidase activity was quenched by exposing slides to 3 % hydrogen peroxide for 10 min. Following 30 min blocking with Biocare blocking reagent (Biocare Medical, Concord, CA) was followed by incubation with primary antibody diluted in the same blocking buffer as follows: Ki-67 (Dako, Carpenteria, CA; 1:200, 4°C overnight); slides were washed with PBS, secondary antibody was applied for 30 min at RT, followed by another three washes with PBS. Diaminobenzidine (DAB) was used to develop the antibody stain followed by a hematoxylin counterstain to visualize nuclei. Slides were scanned and digitized using the Aperio Scanscope System (Scanscope XT; Aperio Technologies, Vista, CA).

Analysis of autophagy

In order to assess autophagy activation, Atg5 +/+ H-Rasexpressing iBMK cells were transfected with a GFP/RFP-LC3 plasmid prior to implantation into nude mice. The extent of fluorescence and number of membrane-bound RFP-LC3 puncta per field in each tumor section was quantified using a fluorescence microscope as previously described [28]. For in vitro analysis of autophagic flux, cells were incubated with 10 μM chloroquine for 24 h before being lysed using radioimmunoprecipitation buffer (RIPA), resolved by SDS-PAGE, transferred to a PVDF membrane and probed with antibodies specific for LC3B (Cell Signaling #2775), Atg5 (Cell Signaling #12994), or β-Actin (Santa Cruz #).

Data analysis

Data are reported as mean ± standard error of the mean (SEM). Comparisons between the diet groups with respect to body composition parameters, serum markers, and tumor weight differences were performed using t test. Changes in tumor volume between diet groups and tumor type were assessed using repeated measures analysis. The proportion of mice with palpable tumors between the autophagy-competent and autophagy-deficient tumors was compared using Fisher's exact test. With the exception of the MRS data, statistical analyses were conducted using either Microsoft Excel software or STATA software (College Station, TX). All t tests were two-tailed, and results were considered significant if p < 0.05.

All the MRS datasets were processed using MetaboLab [38] in the MATLAB programming environment (Math-Works, Inc., Natick, MA). MRS resonances were assigned and the metabolites quantified using the Chenomx NMR Suite and other available libraries [39][40][41][42]. Principal component analysis (PCA) of 1 H-MRS spectra of serum from control and CR mice was performed using STATA.

Results

Effects of CR on body composition parameters, blood glucose, and serum energy balance-related hormones in nude mice A total of 94 female athymic nude mice were administered either a control or CR diet, including a subset of mice (n = 19/diet group) killed at 16 weeks on diet, in order to analyze body composition and serum profiles. Administration of the diet regimens for 16 weeks induced two different body phenotypes in the nude mice (Fig. 1a). Relative to the control diet-fed mice, the CR mice weighed significantly less (p < 0.0001; Fig. 1b); had decreased body fat and bone mineral density (p < 0.01 and p < 0.0001, respectively; Fig. 1c, e); and had greater lean mass (p = 0.01; Fig. 1d).

CR mice displayed significantly lower levels of fasting blood glucose (Fig. 2a), serum insulin (Fig. 2b), serum IGF-1 (Fig. 2c), and serum leptin (Fig. 2d) than the control group, while levels of circulating adiponectin were significantly increased by CR (Fig. 2e; n = 11 randomly selected samples/diet group and p < 0.0001 for all analytes).

Effect of CR and autophagy inhibition on tumor growth

Following 16 weeks of diet regimens, all remaining mice (n = 56) were injected subcutaneously with either autophagy-proficient Atg5 +/+ ;Hras V12 iBMK cells or autophagy-deficient Atg5 −/− ;Hras V12 iBMK cells (14 mice/cell line for each diet). These iBMK cells were derived from Atg5 +/+ and Atg5 −/− mice, respectively. Previous studies established that deletion of Atg5 results in a profound autophagy defect [28,43]. Atg5 deletion and autophagy deficiency was confirmed by western immunoblot analysis for Atg5 and LC3B (Fig. 3a). Unlike Atg5 +/+ cells, incubation with the lysosomal inhibitor chloroquine does not cause accumulation of LC3B-II in Atg5 −/− cells (Fig. 3a).

As shown in Fig 3b, tumor incidence 4 weeks after injection was significantly (p < 0.05) higher in control diet-fed mice with Atg5 +/+ tumors (12/14, 86 %) than Atg5 −/− tumors (5/14; 36 %). In CR-fed mice, Atg5 +/+ tumor incidence was 71 % (10/14;) and Atg5 −/− tumor incidence was 14 % (2/14). Tumor growth (Fig. 3c) and final tumor volume (Fig. 3d) was greatest for Atg5 +/+tumors in control-fed mice, intermediate for Atg5 +/+tumors in CR-fed mice and Atg5 −/− tumors in controlfed mice, and lowest for Atg5 −/− tumors in CR mice.

Tumors excised from CR groups displayed reduced intratumoral adipocytes compared to those from control groups (Fig 3e [i]), and both CR and autophagy deficiency was sufficient to reduce proliferative (Ki67 positive) cells within the tumor (Fig 3e [ii]).

Effect of CR on autophagic flux in tumors

No diet-dependent difference in the percent of cells with LC3 puncta was detected in tumors obtained 4 weeks after mice were transplanted with Atg5 +/+ tumor cells (Fig. 3e). However, RFP-LC3 fluorescence was significantly decreased in tumors from CR mice compared with control-fed mice (Fig. 3f ). Autophagy is a highly dynamic process, and the flux of RFP-LC3 puncta formation and autophagosome degradation at low pH is rapid [44]. Increased RFP-LC3 turnover due to increased autophagic flux in response to CR likely explains the observed reduction in RFP-LC3 fluorescence in the Atg5 +/+ tumors from CR mice.

Effect of CR on serum metabolomic profile

The low rate of tumor development and growth precluded further in vivo analysis of the effect of CR in Atg5 −/− tumors, although previous studies have established that LC3 puncta formation is negligible in Atg5 −/− tumors [28]. To establish a relevant in vitro approach to study the effects of CR and/or autophagy inhibition in Atg5 −/− and Atg5 +/+ cells, we first characterized the impact of CR on the serum metabolomic profile using an untargeted MRS-based metabolomics approach was evaluated in serum samples obtained 4 weeks after mice were transplanted with Atg5 +/+ tumor cells. Figure 4a presents representative magnetic resonance spectra. PCA was performed on the entire MRS dataset (n = 11 serum samples per group) to evaluate the global metabolic changes induced by CR relative to control (Fig. 4b); the scores plot indicates that the serum samples from control and CR mice are characterized by distinct metabolic profiles. A further analysis of the PCA loadings plot in conjunction with the MRS spectra profiles led to the identification of several discriminant metabolites that significantly change in the serum profile of CR mice. The average serum concentration change (as percent change in CR mice compared to control) of the identified, statistically significant (p value after positive false discovery rate correction <0.05) metabolites are shown in Table 1 and highlighted in the MR spectra in Fig. 5c. The serum concentrations of glucose, several amino acids, and Krebs cycle intermediates decreased as a consequence of CR, while ketone bodies (acetoacetate and 3-hydroxybutyrate in particular, and also acetone) increased, suggesting CR resulted in a metabolic switch away from glucose metabolism.

Effect of glucose deprivation on colony formation

Atg5 +/+ and Atg5 −/− cells were grown in media with normal or reduced glucose concentrations to model in vitro the metabolic state induced by control and CR diets in vivo (Fig. 5). Atg5 −/− cells formed fewer colonies than Atg5 +/+ in all conditions, and autophagy deficiency (Atg5 −/− ) combined with low glucose (5, 1 mM) resulted in significantly reduced colony formation.

Discussion

Our findings confirm the hypothesis that combining the systemic metabolic reprogramming that occurs with CR with cell intrinsic autophagy inhibition exerts more potent anticancer effects in vivo and in vitro than either CR or autophagy inhibition alone. We found that administration of a 30 % CR diet to nude mice (in which CR has not been previously well studied) resulted in a significant decrease in body fat, fasting blood glucose, and serum insulin, IGF-1, and leptin levels, while it increased adiponectin levels. Metabolomic analyses of CR-fed nude mice showed a significant decrease in circulating glucose, several amino acids (including alanine, pyruvate, ornithine, and tyrosine), and several Krebs cycle intermediates (including fumarate, 2-oxoglutamic acid) compared to control mice. There was also a concomitant increase in metabolites indicating CR induced negative energy balance, increased fatty acid oxidation, and ketosis, including acetoacetate, 3-hydroxybutyrate, urea, and acetone, consistent with a metabolic shift away from glucose metabolism and toward fat/ketone metabolism during CR.

To assess the separate and interactive effects of CR and autophagy deficiency on tumor development and growth, we used a xenograft model in nude mice subcutaneously injected with iBMK cells derived from Atg5 +/+ or Atg5 −/− mice, immortalized by E1A and dominant-negative p53 expression, stably transfected with a H-Ras G12V expression construct, and a p-tFl-RFP-tandem tagged LC3 expression plasmid [28]. CR, relative to control diet, significantly reduced the growth of transplanted Atg5 +/+ tumors. Similar to CR, autophagy Fig. 2 Effects of CR on blood glucose and serum energy balance-related hormones. Whole blood was collected from a subset of mice on CR or control diet for 16 weeks and analyzed for blood glucose concentration (a). Serum levels of insulin (b), IGF-1 (c), leptin (d), and adiponectin (e) were measured using a BioRad BioPlex analyzer. Data presented are the mean ± SEM (n = 11/diet group) deficiency (Atg5 −/− ) decreased tumor growth in controlfed mice. Tumor growth was lowest in the combined CR and autophagy-deficient group. Tumors derived from mice transplanted with autophagy-competent (Atg5 +/+ ) cells and fed a CR diet displayed increased autophagic flux. Despite several limitations, including the relatively small sample size (n = 14 mice/diet group for each cell line), the low incidence of Atg5 −/− tumors, and the shortcomings associated with xenograft models for studies of diet and cancer [20], these data illustrate three important characteristics of energy/nutrient dependence of many tumors that could inform new strategies for cancer prevention and treatment. First, intracellular sources of nutrients (autophagy) are required for efficient tumor growth. In Fig. 3 Autophagy inhibition and CR reduce tumor growth. a Immunoblot analysis of Atg5 expression and LC3B cleavage in Atg5 +/+ ;Hras V12 iBMK cells or Atg5 −/− ;Hras V12 iBMK cells incubated with or without 10 μM chloroquine (CQ). Atg5 +/+ or effect of autophagy inhibition and CR on tumor incidence (b), tumor growth (c), and final tumor volume (d). Atg5 −/− mice were transplanted into nude mice fed either a control or CR diet (n = 14/diet and genotype), and tumor volume was measured weekly. e Representative H&E (i) and Ki67 IHC staining (ii) images of Atg5 +/+ and Atg5 −/− tumors from mice fed a control or CR diet. f Effect of CR, relative to control diet, on LC3 puncta formation and RFP-LC3 fluorescence in tumors from Atg5 +/+ ;Hras V12 iBMK cell transplants. g Fluorescence microscopy of RFP-LC3 expressing Atg5 +/+ tumor slices from mice fed a control or CR regimen. Graphs presented are mean ± SEM and values with different letters are significantly different at p <0.05 addition, restriction of external substrate (via CR) significantly prohibits tumor growth. Finally, restricting the ability of cells to harness intracellular substrates under conditions of limited external energy and nutrients may have potent anticancer effects.

Metabolic reprogramming is a hallmark of tumorigenesis, and reduced reliance on mitochondrial respiration and increased glycolysis are common in many cancers [45,46]. Associated with this so-called Warburg effect is increased dependence on adequate levels of systemic glucose and other substrates. In this study using metabolomics profiling, we find that mice fed a CR regimen had reduced serum levels of glucose and several amino acids. Reduced circulating glucose and amino acids decreases the external availability of these nutrients to the developing cancer cells, effectively starving the tumor via the external environment. The striking increase of ketone bodies indicates increased fatty acid breakdown as an alternative to carbohydrate metabolism as a primary source of energy in response to CR. As low carbohydrate/ high fat diets, which rewire energy metabolism to utilize ketones, are effective in reducing tumor growth in a number of cancer models [47][48][49], our findings suggest that the beneficial anticancer effects of both CR and ketogenic diets may have common underlying mechanisms.

The observed suppressive effects of CR on tumor growth in this model are consistent with several previous studies in other cancer models [3][4][5][6]15]. CR is known to induce autophagy in a number of normal tissues and organs [50,51], and autophagy activation can promote survival in cancer cells during nutrient deprivation [52][53][54][55]. Our observed inhibitory effect of genetically induced autophagy deficiency on tumor development and growth is also consistent with previous reports of the anticancer effects of autophagy inhibition [23,[25][26][27][28][29][30][31]. However, to date, little research has explored the potential of combining dietary (such as CR) or other interventions that induce negative energy balance with autophagy inhibition as a therapy in the treatment of solid tumors.  Values represent the average serum concentration change (as percent change in serum from CR mice relative to control-fed mice) of the identified, statistically significant metabolites (Fig. 4c), based on p values after positive false discovery rate (pFDR) correction <0.05

One report by Harhaji et al. showed that combining chloroquine (which inhibits autophagy by interfering with lysosome function) with serum starvation resulted in greater growth arrest and cell death relative to either treatment alone in B16 melanoma cells, U251 glioma cells, and L929 fibrosarcoma cells in vitro [56]. Moreover, consistent with our in vivo finding, Harhaji et al. showed that autophagy inhibition (again via chloroquine) and a 30 % total diet restriction in vivo each slowed the growth of transplanted B16 melanoma cells in C57BL/6 mice, and combining chloroquine treatment with the diet restriction inhibited the development and growth of B16 melanoma better than either intervention alone [56]. The in vivo study of B16 melanomas was small (eight mice/treatment group), involved a 30 % total diet restriction (which unlike a CR regimen is subject to vitamin and mineral deficiencies) and did not include analysis of autophagy in the tumors. Nonetheless, the Harhaji et al. study supports our finding that combining autophagy inhibition with CR may be an effective anticancer strategy.

Although to our knowledge there are no other reports in the literature combining CR and autophagy inhibition, further support for this approach of combining autophagy induction in cancer cells with systemic metabolic alterations comes from a study assessing the growth of A-2058 human melanoma cells grown in nude mice receiving a control or leucine-restricted diet, with or without chloroquine treatment [57]. Chloroquine treatment alone had a modest effect on tumor growth, while leucine deprivation alone had little effect on tumor growth. However, the combination of leucine deprivation and chloroquine treatment strongly suppressed melanoma growth [57].

Conclusions

In a murine model of Ras-driven cancer, combined restriction of extracellular (via CR) and intracellular (via autophagy inhibition) sources of energy and nutrients more potently suppresses tumor growth than either CR or autophagy inhibition alone. Similarly, glucose deprivation combined with autophagy deficiency reduced in vitro colony formation more potently than either glucose or autophagy deficiency alone. Thus, interventions targeting both systemic energy balance and tumor-cell intrinsic autophagy may represent a potent anticancer strategy. Current efforts in our laboratory using multiple preclinical models of breast, colon, and pancreatic cancer focus on elucidating the mechanisms underlying the separate and combined effects of CR (or other forms of nutrient deprivation) and autophagy inhibitors, including several emerging autophagy inhibitors currently under clinical development [58].

Fig. 1
1Effects of CR on body composition. Nude mice were fed a control or CR diet for 16 weeks (n = 19/diet group), and body size (a), body weight (b), percent body fat (c), percent lean mass (d), and bone mineral density (e) were compared. Data presented are the mean ± SEM

Fig. 4
4Effect of CR on serum metabolomic profile. a Representative 1D 1 H-MRS spectra of serum isolated from mice fed a control (n = 11) or CR (n = 11) diet for 20 weeks and transplanted with Atg5 +/+ cells. b Scores plots from principal component analysis (PCA) of 1 H-MRS spectra from control and CR serum. c Differential metabolites identified in PCA between diet groups are indicated on 1 H-MRS spectra

Fig. 5
5Effect of glucose restriction on colony formation and growth of Atg5 +/+ and Atg5 −/− cells. a Representative images of colonies formed by Atg5 +/+ and Atg5 −/− cells grown in media containing 10, 5, or 1 mM glucose. Graph is mean ± SEM and is inclusive of three independent experiments

Table 1
1Effect of CR on serum metabolitesMetabolite % of control serum P value after pFDR correctionGlucose
42.0
5.5E−08

Lysine
47.2
2.4E−07

Lactate
50.8
3.1E−07

Alanine
52.0
5.53E−08

Pyruvate
57.3
5.6E−06

Tyrosine
57.7
1.07E−06

Fumarate
60.4
0.008

2-Oxoglutarate
61.9
0.023

Ornithine
66.1
0.018

Acetoacetate
842.2
4.1E−04

3-Hydroxybutyrate
500.2
4.1E−04

Urea
164.8
0.004

Acetone
152.8
0.017

AcknowledgementsThe authors thank Drs. Anne Hamacher-Brady and Marcie Hursting for the helpful comments.FundingThis study was supported in part by a grant from the Breast Cancer Research Foundation (SDH) and R35 CA197627 (SDH). Support for the NMR facility was provided by the University of Texas Health Science Center at San Antonio (UTHSCSA) and National Institutes of Health Grant NCI P30 CA54174 (CTRC at UTHSCSA). E. White is supported by the NIH under award numbers R01 CA163591, R01 CA130893, R01 CA188096, and R01 CA193970, in addition to P30 CA72720 to the Rutgers Cancer Institute of New Jersey. J Guo is supported by the NIH under award number K22 CA190521-01 and by P30 CA72720 to the Rutgers Cancer Institute of New Jersey.• We accept pre-submission inquiries • Our selector tool helps you to find the most relevant journal Submit your next manuscript to BioMed Central and we will help you at every step:
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Evaluation of a Web-Based App Demonstrating an Exclusionary Algorithmic Approach to TNM Cancer Staging

MDMatthew Kim firstname.lastname@example.com
Brigham and Women's Hospital
Division of Endocrinology, Diabetes and Hypertension
Brigham and Women's Hospital Division of Endocrinology, Diabetes and Hypertension
221 Longwood Avenue RF29102115Boston, BostonMA, MAUnited States, United States

MDMatthew Kim
Brigham and Women's Hospital
Division of Endocrinology, Diabetes and Hypertension
Brigham and Women's Hospital Division of Endocrinology, Diabetes and Hypertension
221 Longwood Avenue RF29102115Boston, BostonMA, MAUnited States, United States

Evaluation of a Web-Based App Demonstrating an Exclusionary Algorithmic Approach to TNM Cancer Staging
10.2196/cancer.4019Original Paper Corresponding Author:
Background: TNM staging plays a critical role in the evaluation and management of a range of different types of cancers. The conventional combinatorial approach to the determination of an anatomic stage relies on the identification of distinct tumor (T), node (N), and metastasis (M) classifications to generate a TNM grouping. This process is inherently inefficient due to the need for scrupulous review of the criteria specified for each classification to ensure accurate assignment. An exclusionary approach to TNM staging based on sequential constraint of options may serve to minimize the number of classifications that need to be reviewed to accurately determine an anatomic stage.Objective: Our aim was to evaluate the usability and utility of a Web-based app configured to demonstrate an exclusionary approach to TNM staging.Methods:Internal medicine residents, surgery residents, and oncology fellows engaged in clinical training were asked to evaluate a Web-based app developed as an instructional aid incorporating (1) an exclusionary algorithm that polls tabulated classifications and sorts them into ranked order based on frequency counts, (2) reconfiguration of classification criteria to generate disambiguated yes/no questions that function as selection and exclusion prompts, and (3) a selectable grid of TNM groupings that provides dynamic graphic demonstration of the effects of sequentially selecting or excluding specific classifications. Subjects were asked to evaluate the performance of this app after completing exercises simulating the staging of different types of cancers encountered during training.Results: Survey responses indicated high levels of agreement with statements supporting the usability and utility of this app. Subjects reported that its user interface provided a clear display with intuitive controls and that the exclusionary approach to TNM staging it demonstrated represented an efficient process of assignment that helped to clarify distinctions between tumor, node, and metastasis classifications. High overall usefulness ratings were bolstered by supplementary comments suggesting that this app might be readily adopted for use in clinical practice.Conclusions:A Web-based app that utilizes an exclusionary algorithm to prompt the assignment of tumor, node, and metastasis classifications may serve as an effective instructional aid demonstrating an efficient and informative approach to TNM staging.

Introduction

The tumor/node/metastasis (TNM) staging system collaboratively developed and maintained by the American Joint Committee on Cancer and the International Union for Cancer Control plays a critical role in the evaluation and management of patients diagnosed with a range of different types of cancers [1]. Accurate staging based on assessment of the extent of anatomic spread of cancer at the time of diagnosis helps to determine prognosis based on correlated survival rates. Staging also helps to guide the planning of treatment, facilitates communication between providers working in different disciplines, and serves as the basis for identifying patients who may be eligible for enrollment in clinical trials [2].

Criteria for stage assignments have been established for 47 different types of cancers. Determination of a patient's stage is based on the classification of three principal components that may be assessed at the point of diagnosis to determine a clinical stage, or after definitive surgery to determine a pathologic stage. Assignment of a tumor (T) classification ranging from T0-T4(a-d) is based on assessment of the size and extent of contiguous spread of the primary tumor. Assignment of a node (N) classification ranging from N0-N3(a-c) is based on assessment of the extent of spread of tumor to regional draining lymph nodes. Assignment of a metastasis (M) classification ranging from M0-M1(a-b) is based on assessment of the presence or absence of distant metastases (Table 1).

Additional prognostic factors that have proven to be significant in the staging of specific types of cancers include tumor grade, tumor location, mitotic rate, risk factors, histologic scores, and biochemical tumor marker levels. Compiled groupings of T, N, M, and prognostic factor classifications are sorted into tabular arrays that are stratified to define stages characterized as anatomic stages or prognostic groups ranging from 0-IV(A-C) in order of declining prognosis ( Table 2).

Carcinoma in situ Tis

Tumor 3 cm or less in greatest dimension, surrounded by lung or visceral pleura, without bronchoscopic evidence of invasion more proximal than the lobar bronchus (ie, not in the main bronchus)

T1

Tumor 2 cm or less in greatest dimension T1a

Tumor more than 2 cm but 3 cm or less in greatest dimension T1b Tumor more than 3 cm but 7 cm or less or tumor with any of the following features (T2 tumors with these features are classified T2a if 5 cm or less): involves main bronchus, 2 cm or more distal to the carina; invades visceral pleura (PL1 or PL2); associated with atelectasis or obstructive pneumonitis that extends to the hilar region but does not involve the entire lung T2 Tumor more than 3 cm but 5 cm or less in greatest dimension T2a

Tumor more than 5 cm but 7 cm or less in greatest dimension T2b

Tumor more than 7 cm or one that directly invades any of the following: parietal pleural (PL3) chest wall (including superior sulcus tumors), diaphragm, phrenic nerve, mediastinal pleura, parietal pericardium; or tumor in the main bronchus less than 2 cm distal to the carina but without involvement of the carina; or associated atelectasis or obstructive pneumonitis of the entire lung or separate tumor nodule(s) in the same lobe T3 Tumor of any size that invades any of the following: mediastinum, heart, great vessels, trachea, recurrent laryngeal nerve, esophagus, vertebral body, carina, separate tumor nodule(s) in a different ipsilateral lobe T4

Regional lymph nodes (N)

Regional nodes cannot be assessed NX No regional lymph node metastasis N0 Metastasis in ipsilateral peribronchial and/or ipsilateral hilar lymph nodes and intrapulmonary nodes, including involvement by direct extension  The conventional approach to staging involves (1) selecting appropriate T, N, M, and prognostic factor classifications, (2) combining these classifications to generate a TNM grouping, and (3) locating this TNM grouping in the array of possible combinations to determine a corresponding stage. This combinatorial approach is inherently inefficient due to the fact that successive tumor and node classifications for different types of cancers are not always graded or mutually exclusive. Assignment of a T1 classification may be based on measurement of the diameter of the primary tumor, while assignment of a T2 classification may be based on identification of a pattern of local invasion. Assignment of an N2 classification may be based on confirmation of spread of tumor to a specific group of regional draining lymph nodes, while assignment of an N3 classification may be based on tabulation of the number of involved lymph nodes. As a result, accurate staging relies on scrupulous review of the criteria for each T, N, and prognostic factor classification to ensure that correct assignments are made.

An alternative approach to staging that seeks to optimize the efficiency of the process is predicated on the notion that unambiguous selection or exclusion of a T, N, M, or prognostic factor classification may serve to constrain the number of subsequent classifications that need to be reviewed to identify a correct TNM grouping. If a specific T, N, M, or prognostic factor classification can be selected based on review of its criteria, then any TNM groupings that do not include that classification can be excluded from further consideration. Alternatively, if a specific classification can be excluded without reservation, then any TNM groupings that include that classification can be excluded from further consideration. The set of TNM groupings that remain as viable options after a specific classification has been selected or excluded will most often encompass a restricted subset of classifications. In its elaboration, this exclusionary approach may effectively serve to minimize the number of classifications that need to be reviewed to accurately determine a patient's stage.

Methods

A Web-based app configured to demonstrate this approach to staging was developed as an instructional aid for trainees. A version coded in ActionScript 3.0 was iteratively adapted to incorporate the following key features [3]:

• an exclusionary algorithm that cycles through a sequence of (1) polling the set of TNM groupings listed for each anatomic stage to tabulate the number of times that each T, N, M, or prognostic factor classification is listed, (2) sorting the tabulated classifications in ascending order of frequency prioritized based on the extent of spread (M >N > T) and level of classification (N3 > N2 > N1> N0), (3) prompting selection or exclusion of the first ranked classification, and (4) excluding nullified TNM groupings from the set based on the response • a grid of anatomic stage listings with corresponding TNM groupings incorporating selectable T, N, M, and prognostic factor classifications • reconfiguration of the criteria specified for T, N, M, and prognostic factor classifications to generate yes/no questions phrased to (1) itemize the components of complex definitions, (2) disambiguate definitions that incorporate combined Boolean AND + OR conditions, and (3) minimize negative definitions (Table 3) Tumor more than 7 cm or one that directly invades any of the following: parietal pleural (PL3) chest wall (including superior sulcus tumors), diaphragm, phrenic nerve, mediastinal pleura, parietal pericardium; or tumor in the main bronchus less than 2 cm distal to the carina but without involvement of the carina; or associated atelectasis or obstructive pneumonitis of the entire lung or separate tumor nodule(s) in the same lobe T3: Complex definition Is the primary tumor >3 cm and ≤7 cm in greatest dimension?

OR Is it associated with any of these findings: Tumor more than 3 cm but 7 cm or less or tumor with any of the following features (T2 tumors with these features are classified T2a if 5 cm or less): involves main bronchus, 2 cm or more distal to the carina; invades visceral pleura (PL1 or PL2); associated with atelectasis or obstructive pneumonitis that extends to the hilar region but does not involve the entire lung Users are prompted to select a tumor type to begin a staging exercise. Some tumor types require selection of secondary options which may include identification of a tumor subtype, anatomic location, age limit, or phase of staging (clinical vs pathologic). Selection of a tumor type shifts to a display that includes a grid of anatomic stage listings and a prompted yes/no question corresponding to the first ranked T, N, M, or prognostic factor classification (Figure 1).

Clicking a Yes or

No button to answer the question will select or exclude the classification. If a specific classification is known at the point of entry, it can be directly selected by clicking on a corresponding entry in the grid of anatomic stage listings. Selection or exclusion of a classification triggers fading of nullified TNM groupings and cycling of the exclusionary algorithm. Subsequent prompted yes/no questions will appear in sequence until the set of TNM groupings has been narrowed to delimit a single TNM grouping and/or a specific anatomic stage. If a specific anatomic stage has been delimited with multiple TNM groupings that persist as viable options, a "Complete" button can be clicked to prompt further selection and exclusion. When a single TNM grouping has been identified, a terminal display lists the anatomic stage and T, N, M, and prognostic factor classifications with their corresponding criteria. Forward and back arrows can be clicked to scroll through the sequence of classifications with highlighting of selected answers and concordant enhancement and fading of associated TNM groupings.

Recruitment

An evaluation study was conducted to assess the usability and utility of this app. Participating subjects recruited from regional clinical training programs by email solicitation included 14 internal medicine residents, 9 surgery residents, and 7 oncology fellows. Institutional review board approval was obtained prior to recruitment and enrollment. Subjects were provided with open access to a fully functional version of the app along with a set of basic operating instructions. They were asked to evaluate its performance during staging exercises that included assessment of a newly diagnosed cancer, restating of recurrent cancer, and review of an incorrectly staged cancer. Subjects were asked to complete these exercises with an eye towards assessment of how they might use this app to (1) simulate the staging of different types of cancers encountered during training and (2) study for board exams that test knowledge of TNM staging criteria. After completing the staging exercises, subjects were asked to complete a Web-based survey about the app focused on rating its ease of use, clarity of presentation, perceived accuracy, and potential for adoption in different clinical and educational settings. The survey consisted of 12 statements phrased to support the usability and utility of the app. Selectable responses were arrayed on a 5-point scale with options labeled "Strongly disagree", "Disagree", "Undecided", "Agree", and "Strongly agree".

Statistical Analysis

Median Likert scores and 25th and 75th quartiles were calculated for each survey item assigning a value of 1 to responses scored as "Strongly disagree", 3 to items scored as "Undecided", and 5 to items scored as "Strongly agree". Subjects were also asked to rate the overall usefulness of the app on a scale ranging from 1 ("Not at all useful") to 10 ("Essential") and were provided with the option of entering free text comments about what they did or did not like about the app.

Results

All 30 of the enrolled subjects completed the entire survey. Subjects expressed a high level of agreement with each of the 12 statements. No "Strongly disagree" responses were registered. The median level of agreement was "Agree" for 7 of 12 statements and "Strongly agree" for the remainder with minimal dispersion (Table 4). It was easy to learn how to use this app.

4-5 4

It is easy to navigate between different sections.

4-5 5

The information presented is clearly organized.

4-5 4

Presenting definitions in the form of Yes/No questions helps to clarify distinctions between different T, N, M, and prognostic factor classifications.

4-5 4

Breaking down complex definitions into sets of questions linked by and/or statements helps to clarify distinctions between different T, N, M, and prognostic factor classifications.

4-5 4

The graphic display helps to clarify distinctions between different anatomic stages.

4-5 4

The ability to review prior answers in sequence helps to clarify distinctions between different anatomic stages. 4-5 4.5 The approach to staging promoted by this app is efficient.

4-5 4

The anatomic stages assigned through use of this app are accurate and reliable.

3-5 4

This app would be a useful instructional aid to help prepare for board certification and re-certification exams.

4-5 5

This app would be a useful resource in clinical practice.

4-5 5

Providers who do not stage patients on a regular basis would be able to use this app without difficulty.

The median overall usefulness rating was 9 (25-75 interquartile range 8-9.75). Eighteen subjects elected to enter free text comments that ranged from general impressions of the app to specific criticisms of its navigability and functionality. While most of the general impressions were favorable ("Great application", "Very useful clinically"), a few subjects expressed reservations about the limited scope of the app (eg, "It would be nice to have links to the appropriate staging guidelines or references", "It would be great to see some of the hematologic malignancies like myeloma and lymphoma added"). The majority of the specific criticisms focused on problems that subjects experienced when trying to use the browser back button to navigate between screens. This problem is commonly encountered with the first use of platform-independent apps that run in browser plug-ins [4]. Internal navigation controls were moved to more intuitive locations in subsequent iterations as a result. A few of the subjects found staging exercises that began with prompted questions about the presence of distant metastases to be disconcerting at first, but on reflection they expressed an understanding of the logic of this approach.

Discussion

Principal Findings

Studies conducted to assess the validity of TNM staging after the most recent revision of specified criteria have demonstrated high levels of correlation between accurately assigned anatomic stages and overall survival for a range of different cancer types [5][6][7][8][9][10][11][12][13][14]. In light of the critical role that TNM staging has come to play in the treatment of cancer, it is curious that there do not appear to have been any published studies investigating the approaches that providers adopt to assign anatomic stages. Most of the studies evaluating TNM staging have focused on assessing rates of completion and accuracy of assignment without any examination of the process itself. Inventories of tumor registries have revealed that providers treating patients with specific types of cancers do not always assign anatomic stages or track the information needed to retrospectively confirm accurate assignments [15][16][17]. Studies that have compared assigned anatomic stages to adjudicated anatomic stages have shown that the accuracy of assignment may vary based on the expertise levels of providers and the specific types of cancers under consideration [18][19][20][21].

Resources that have been developed to assist providers engaged in the task of assigning anatomic stages include printed worksheets, encoded spreadsheets, wizards incorporated in electronic medical records, and an array of apps developed to run on smartphones and tablets [1, [22][23][24][25][26][27][28]. While the controls and interfaces that they present vary to an extent, these resources universally implement combinatorial approaches to staging that rely on the selection of discrete T, N, M, and prognostic factor classifications to determine an anatomic stage. While the automated linkage of TNM groupings to anatomic stages provided by coded apps may ensure greater accuracy of assignment that obviates the need to refer to tabular arrays, users still need to review the criteria for each T, N, and prognostic group classification to ensure that correct groupings have been identified.

Conclusion

This evaluation study demonstrated the perceived utility of a Web-based app configured to demonstrate an exclusionary approach to TNM staging. Subjects recruited from a pool of target users found that it was easy to use, and they deemed the approach to assignment that it employed to be informative, efficient, accurate, and reliable. It was interesting to note that while this app was originally developed as an educational resource, the statement that elicited the greatest number of "Disagree" responses focused on its potential use as an instructional aid to help prepare for board certification and re-certification exams. By way of contrast, statements suggesting that it could be used in clinical practice by providers with varying degrees of expertise elicited greater numbers of "Strongly agree" responses. This feedback may guide further development and investigation that may focus on evaluating the performance and acceptance of this app when it is deployed for use in simulated cancer staging exercises and real-time clinical practice.

N1Metastasis in ipsilateral mediastinal and/or subcarinal lymph node(s) N2 Metastasis in contralateral mediastinal, contralateral hilar, ipsilateral or contralateral scalene, or supraclavicular lymph node(nodule(s) in a contralateral lobe; tumor with pleural nodules or malignant pleural (or pericardial) effusion M1aDistant metastasis M1b

Figure 1 .
1Clicking a response to the prompted question triggers fading of nullified TNM groupings. Forward and back arrows can be clicked to scroll through the sequence of selected and excluded classifications.

Table 1 .
1T, N, and M classifications for cancer of the lung.Definition
Classification

Primary tumor (T)

Primary tumor cannot be assessed
TX

No evidence of primary tumor
T0

Table 2 .
2Anatomic stage/prognostic groups for cancer of the lung.M
N
T
Stage

M0
N0
Tx
Occult

M0
N0
Tis
0

M0
N0
T1a
IA

M0
N0
T1b

M0
N0
T2a
IB

M0
N0
T2b
IIA

M0
N1
T1a

M0
N1
T1b

M0
N1
T2a

M0
N1
T2b
IIB

M0
N0
T3

M0
N2
T1a
IIIA

M0
N2
T1b

M0
N2
T2a

M0
N2
T2b

M0
N1
T3

M0
N2
T3

M0
N0
T4

M0
N1
T4

M0
N3
T1a
IIIB

M0
N3
T1b

M0
N3
T2a

M0
N3
T2b

M0
N3
T3

M0
N2
T4

M0
N3
T4

M1a
Any N
Any T
IV

M1b
Any N
Any T

Table 3 .
3Reconfiguration of classification criteria for cancer of the lung.Does it involve the main bronchus at a site that is >2 cm distal to the carina without involvement of the carina? OR Is it associated with atelectasis or obstructive pneumonitis of the entire lung?Yes/No question
Definition
Classification

Is the primary tumor >7 cm in greatest dimen-
sion?

OR

Does it invade any of these structures: Parietal
pleura; Chest wall (including the superior sul-
cus); Diaphragm; Phrenic nerve; Mediastinal
pleura; Parietal pericardium

OR

OR

Is there a separate tumor nodule in the same
lobe?

T2 :
T2Combined Boolean AND + OR conditions•
Involvement of the main bronchus at a site
that is ≥2 cm distal to the carina;
•
Invasion of the visceral pleura;
•
Atelectasis or obstructive pneumonitis that
extends to the hilar region but does not in-
volve the entire lung

Is there evidence of distant metastasis?
No distant metastasis
M0: Negative definition

Table 4 .
4Survey responses.25-75 interquartile
range

Median

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Conflicts of InterestNone declared. This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Cancer, is properly cited. The complete bibliographic information, a link to the original publication on http://cancer.jmir.org/, as well as this copyright and license information must be included.Abbreviations
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