total number of figures and tables: 7 (7 figures) · 2018. 2. 28. · drug-sensitive, but not...

1

Title: Metabolic determinants of sensitivity to phosphatidylinositol 3-kinase 1

pathway inhibitor in small-cell lung carcinoma 2

3

Hideki Makinoshima1,2

, Shigeki Umemura3*

, Ayako Suzuki1, Hiroki Nakanishi

4, Ami 4

Maruyama2, Hibiki Udagawa

3, Sachiyo Mimaki

1, Shingo Matsumoto

1,3, Seiji Niho

3, 5

Genichiro Ishii5, Masahiro Tsuboi

6, Atsushi Ochiai

5, Hiroyasu Esumi

7, Takehiko Sasaki

4, 6

Koichi Goto3, Katsuya Tsuchihara

1 7

8

1Division of Translational Research, Exploratory Oncology Research & Clinical Trial 9

Center, National Cancer Center, Kashiwa, Chiba, Japan 10

2Tsuruoka Metabolomics Laboratory, National Cancer Center, Tsuruoka, Japan 11

3Department of Thoracic Oncology, National Cancer Center Hospital East, Kashiwa, 12

Chiba, Japan 13

4Research Center for Biosignal, Akita University, Akita, Japan 14

5Division of Pathology, Exploratory Oncology Research & Clinical Trial Center, 15

National Cancer Center, Kashiwa, Chiba, Japan 16

6Department of Thoracic Surgery, National Cancer Center Hospital East, Kashiwa, 17

Chiba, Japan 18

Research. on January 7, 2021. © 2018 American Association for Cancercancerres.aacrjournals.org Downloaded from

Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on February 28, 2018; DOI: 10.1158/0008-5472.CAN-17-2109

http://cancerres.aacrjournals.org/

2

7Division of Clinical Research, Research Institute for Biomedical Sciences, Tokyo 1

University of Science, Noda, Chiba, Japan 2

3

Running title: Metabolic determinants of sensitivity to PI3K inhibitor 4

5

Keywords: Small cell lung cancer, Metabolomics, PI3K/AKT/mTOR pathway, 6

Purine-related metabolites, Phosphatidylinositol (3,4,5)-trisphosphate subspecies 7

8

*Corresponding author: Shigeki Umemura, MD, PhD 9

Department of Thoracic Oncology, National Cancer Center Hospital East, Kashiwa, 10

Chiba, 277-8577, Japan 11

E-mail: [email protected] 12

Phone: +81-4-7133-1111, Fax: +81-4-7131-4724 13

14

Conflict of interest: Gedatolisib was obtained with a material transfer agreement 15

(MTA) from Pfizer. Shigeki Umemura, Hibiki Udagawa, Seiji Niho and Koichi Goto 16

have received Gedatolisib from Pfizer. Shingo Matsumoto, Seiji Niho and Koichi Goto 17

have received research grants from Pfizer. 18



mailto:[email protected]


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1

Manuscript Body Word Count: 5382 2

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Total Number of Figures and Tables: 7 (7 figures)4




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Abstract (197/250 words) 1

Comprehensive genomic analysis has revealed that the PI3K/AKT/mTOR pathway is a 2

feasible therapeutic target in small-cell lung carcinoma (SCLC). However, biomarkers 3

to identify patients likely to benefit from inhibitors of this pathway have not been 4

identified. Here we show that metabolic features determine sensitivity to the 5

PI3K/mTOR dual inhibitor gedatolisib in SCLC cells. Substantial phosphatidyl lipid 6

analysis revealed that a specific phosphatidylinositol (3,4,5)-trisphosphate (PIP3) 7

subspecies lipid product: PIP3 (38:4) is predictive in assessing sensitivity to 8

PI3K/mTOR dual inhibitor. Notably, we found that higher amounts of purine-related 9

aqueous metabolites such as hypoxanthine, which are characteristic of SCLC biology, 10

lead to resistance to PI3K pathway inhibition. In addition, the levels of the mRNA 11

encoding hypoxanthine phosphoribosyl transferase 1 (HPRT1), a key component of the 12

purine salvage pathway, differed significantly between SCLC cells sensitive or resistant 13

to gedatolisib. Moreover, complementation with purine metabolites could reverse the 14

vulnerability to targeting of the PI3K pathway in SCLC cells normally sensitive to 15

gedatolisib. These results indicate that the resistance mechanism of PI3K pathway 16

inhibitors is mediated by the activation of the purine salvage pathway, supplying purine 17




5

resource to nucleotide biosynthesis. Metabolomics is a powerful approach for finding 1

novel therapeutic biomarkers in SCLC treatment. 2

3

Significance 4

Findings identify features that determine sensitivity of SCLC to PI3K pathway 5

inhibition and support metabolomics as a tool for finding novel therapeutic biomarkers. 6

7

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14

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Introduction 1

Small cell lung cancer (SCLC) accounts for approximately 14% of all lung cancers. It is 2

an exceptionally aggressive neuroendocrine tumor (NET) with a high proliferative 3

index and a strong predilection for early metastasis(1, 2). Basic and clinical research 4

have produced little innovation in the treatment of SCLC over the past 30 years(3-5). 5

Recent studies reporting a comprehensive genomic analysis of SCLC suggested that 6

transcriptional deregulation might play a role in SCLC biology, and concluded that 7

genomic research manifested fewer drug targets in SCLC(6-9). 8

The phosphatidylinositol-3-kinase/serine-threonine kinase/mammalian target of 9

rapamycin (PI3K/AKT/mTOR) signaling pathway plays a key role in cell proliferation, 10

growth, survival, and protein synthesis(10-13). It provides not only strong growth and 11

survival signals to tumor cells but also has profound effects on glucose and amino acid 12

metabolism(13-17). Class 1 PI3Ks play a key role in the biology of human cancer. They 13

are responsible for the production of phosphatidylinositol 3-monophosphate (PI[3]P1), 14

phosphatidylinositol (3,4)-bisphosphate (PI[3,4]P2), and phosphatidylinositol 15

(3,4,5)-trisphosphate (PI[3,4,5]P3)(12, 18). The gene encoding the PI3K-α isoform 16

(PIK3CA) is mutated or amplified in a wide range of cancers(10, 12). Since the 17

PI3K/AKT/mTOR pathway is distinguishable among SCLC genomic alterations(19-21), 18



https://en.wikipedia.org/wiki/Phosphatidylinositol_3-phosphate

https://en.wikipedia.org/wiki/Phosphatidylinositol_(3,4)-bisphosphate

https://en.wikipedia.org/wiki/Phosphatidylinositol_(3,4,5)-trisphosphate

https://en.wikipedia.org/wiki/Phosphatidylinositol_(3,4,5)-trisphosphate


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it may be a feasible chemotherapeutic target. An investigator-initiated Phase II study of 1

gedatolisib (PF-05212384, PKI-587) in advanced recurrent SCLC patients harboring 2

activating mutations in this pathway is ongoing (University Hospital Medical 3

Information Network trial number. 000020585). Gedatolisib is a highly potent dual 4

inhibitor of PI3K and mTOR and it has shown excellent activity in vitro and in vivo, 5

with antitumor efficacy in both subcutaneous and orthotopic xenograft tumor models 6

when administered intravenously(22). Its efficacy, pharmacokinetic, and safety profiles 7

are favorable, and it has advanced to phase I clinical evaluation(23) and the agent 8

afforded acceptable tolerability and activity in patients with recurrent endometrial 9

cancer in a phase II study(24). 10

Metabolomics, which can be defined as measurement of the levels of all 11

cellular metabolites, is a comprehensive assessment of endogenous metabolites. It 12

attempts to systematically identify metabolites from clinical samples(25-27). 13

Dysregulated metabolism is a hallmark of cancer, with numerous metabolic alterations 14

found in tumor cells with respect to aerobic glycolysis, reduced oxidative 15

phosphorylation, and increased generation of the biosynthetic intermediates needed for 16

cell growth and proliferation(28-34). Metabolomics has been applied to cancer 17

diagnosis and drug discovery with the goal of identifying pathways linked to cancer 18




8

progression. We recently reported that treatment with kinase inhibitors altered levels of 1

numerous metabolites in drug-sensitive, but not drug-resistant, lung adenocarcinoma 2

cells(15, 35). 3

The development of new and more potent PI3K/AKT/mTOR-targeted therapies 4

for SCLC requires the availability of predictive biomarkers able to select the most 5

effective therapies for different patients. Here, we show that the PI3K/mTOR dual 6

inhibitor gedatolisib effectively represses SCLC tumor growth in vivo, and that 7

metabolic biomarker levels can predict the response to this PI3K/mTOR inhibitor. 8

9

Materials and Methods 10

Patient Samples 11

This study has been performed in compliance with the ethical standards as laid down in 12

the 1964 Declaration of Helsinki and its later amendments or comparable ethical 13

standards, and Japanese ethical guidelines for human genome/gene analysis research 14

issued by Ministry of Education, Culture, Sports, Science and Technology, and Ministry 15

of Health, Labour and Welfare of Japan. All clinical specimens were collected from 16

patients after obtaining written informed consent for the use of their biological material. 17




9

The National Cancer Center Institutional Review Board approved this study. The IRB 1

approval number of this study is 2011-201. 2

3

Metabolite Measurements 4

Tumor tissues and surrounding non-tumor tissues were surgically resected or acquired 5

from biopsies of SCLC patients. The tumor specimens comprised ≥40% tumor cells. 6

Unpaired non-tumor lung tissues were pathologically proven non-malignant tissues 7

obtained from pulmonary nodules or tumors. We used adenocarcinoma cell lines to 8

compare the metabolic profiles of SCLCs to those of adenocarcinomas(35). Xenograft 9

SCLC tumors were resected from mice. The resected tissue samples were immediately 10

frozen in liquid nitrogen, weighed, and stored at -80°C until metabolite extraction. 11

Metabolic extracts were prepared from 1–10 × 106 SCLC cells with methanol 12

containing Internal Standard Solution (Human Metabolome Technologies; HMT, Inc., 13

Tsuruoka, Japan) and analyzed using a capillary electrophoresis (CE)-connected 14

electrospray ionization/time-of-flight mass spectrometry (ESI-TOFMS) and capillary 15

electrophoresis tandem mass spectroscopy (CE-MS/MS) system (CARCINOSCOPE, 16

Human Metabolic Technologies, Boston MA, USA). Procedures for metabolite 17




10

measurements were as previously described(15, 35) and are described in more detail in 1

the Supplementary Information. 2

3

Phosphoinositide Measurements 4

Cells were placed in lysis buffer on ice and sonicated. The protein content of 5

supernatants was quantified and a solution containing 3 mg protein was used for further 6

experiments. Phosphatidylserine (PS), Phosphatidylinositol (PI), PI monophosphate 7

(PIP1), PI bisphosphate (PIP2), and PI trisphosphate (PIP3), possessing 4 or 37 carbon 8

atoms in their fatty acid components were used as internal standards for quantification. 9

For quantification of phosphatidyl lipids, acidic phospholipids were extracted from lung 10

tissues by the Bligh–Dyer method as described previously(36). PIP3 was measured by 11

modifying a method developed by Clark and colleagues(36, 37). Briefly, column 12

chromatography was performed using an UltiMate 3000 LC system (Thermo Fisher 13

Scientific) equipped with an HTC PAL autosampler (CTC Analytics), while 14

electrospray ionization MS/MS analysis was conducted using a TSQ-Vantage AM 15

(Thermo Fisher Scientific) at the Akita Lipid Technologies (ALTe)(36). 16

17

Animal Studies 18




11

All experimental BALB/c-nu mice were handled in compliance with institutional 1

guidelines established by the Animal Care Committee of the National Cancer Center 2

Hospital East. All animal experiments were approved by the Animal Care Committee of 3

the National Cancer Center Hospital East. H1048 and SBC-5 SCLC cells were injected 4

into the subcutaneous tissue of nude mice (6 weeks of age, Charles River, Japan). 5

Tumor volume was calculated as the product of a scaling factor of 0.52 and the tumor 6

length, width, and height were measured every four days. For these animal experiments, 7

gedatolisib compound was obtained from Pfizer Global Research and Development 8

(New York, NY, USA). After tumor sizes were > 100 mm3, gedatolisib (10 mg/kg) 9

treatment was started. Gedatolisib or vehicle was administered intravenously every 4 10

days. For IHC analysis, organs were obtained from mice at the end of experiments and 11

fixed in 10% formalin. Xenograft SCLC tumors resected from mice at 6 hours post 12

gedatolisib or vehicle treatment were used for metabolite measurements. 13

14

Statistical analysis 15

We used Student’s t-test, Welch's t test and two-sided Mann-Whitney U tests. 16

Mann-Whitney U tests were conducted by SPSS version 22. A p value < 0.05 was 17




12

considered statistically significant unless stated otherwise. We used Ward's hierarchical 1

clustering method to classify gedatolisib-sensitive and -resistant cell lines. 2

3

MSEA 4

MSEA was performed according to a published protocol(38). Briefly, the targeted 5

metabolites were classified into 16 categories according to their metabolic pathways 6

(e.g. glycolysis) or metabolite groups (e.g. essential amino acids). MSEA was 7

performed using R (v2.15.3) with the mseapca package 8

(https://cran.r-project.org/web/packages/mseapca/). The classification of metabolic 9

pathways is listed in Supplementary Table S1. 10

11

Results 12

Genomic alterations and sensitivity to gedatolisib in SCLC cell lines 13

To investigate the efficacy of a PI3K/AKT/mTOR pathway inhibitor against SCLC, we 14

investigated whether alterations in this pathway enhanced the sensitivity of SCLC cell 15

lines to the PI3K/mTOR dual inhibitor gedatolisib. 16

We analyzed the in vitro sensitivity of SCLC cell lines to the dual PI3K/mTOR 17

kinase inhibitor gedatolisib through proliferation assays. The genomic profiles of the 18




13

SCLC cell lines used in this paper are shown (Supplementary Table S2). IC50 values 1

were measured in 42 SCLC cell lines, and found to range from 5 nM to ≥1 μM (Fig. 2

1A). We previously reported that NCI-H1048 cells harboring an activating mutation in 3

the PIK3CA gene (H1047R) were the most sensitive to BEZ235 (PI3K/mTOR dual 4

inhibitor), with an IC50 of 5.4 nM(20). Consistent with this result, we confirmed that 5

H1048 was the SCLC cell line most sensitive to gedatolisib treatment. We found that 6

SCLC cell lines could be categorized into two groups according to their susceptibility to 7

gedatolisib treatment: four of the cell lines (H1048, DMS114, H187 and H446) were 8

sensitive (mean IC50 = 14.9 nM), with an IC50 value after 72 hours of treatment of ≤ 23.6 9

nM, whereas the other 38 SCLC cell lines were resistant to gedatolisib treatment (mean 10

IC50 = 219.5 nM), with an IC50 value after 72 hours of treatment of ≥ 45.8 nM (Fig. 1A). 11

Half of the gedatolisib-sensitive cell lines (H1048 and H446) harbored genomic 12

alterations in the signaling molecules of the PI3K pathway (PIK3CA H1047R mutation 13

and PTEN deletion), as did 14 gedatolisib-resistant SCLC cell lines (e.g., SBC-5 and 14

H1694) (Fig. 1A). Two cell lines, DMS53 which harbors the KRAS amplification, and 15

SHP77 which harbors the KRAS G12V mutation, were extremely resistant to gedatolisib, 16

suggesting that activation of the KRAS pathway might induce a gedatolisib-resistant 17

phenotype in SCLC cell lines. No correlation was observed between gedatolisib 18




14

sensitivity and copy number gain in the MYC family gene, which is widely distributed 1

in SCLC cell lines (Fig. 1A). Likewise, there was no apparent relationship between 2

gedatolisib sensitivity and mutation or amplification/deletion of the major cancer genes, 3

such as RB1 or TP53, among the SCLC cell lines (Supplementary Table S2). In addition, 4

there was no correlation between gedatolisib sensitivity and sensitivity to the cytotoxic 5

agent etoposide. (Fig. 1B). 6

7

The phosphorylation status of proteins and sensitivity to gedatolisib in SCLC cell 8

lines 9

To define the connectivity of protein phosphorylation networks, we quantitatively 10

delineated the PI3K and mTOR network in SCLC cell lines by mass spectrometry-based 11

phosphoproteomics. We quantitated levels of phospho-peptides using this proteomic 12

approach after H1048 cells were treated with DMSO vehicle or gedatolisib (100 nM) 13

for 1h in triplicate. A total of 4185 phosphorylated peptides were identified in H1048 14

cells under these experimental conditions (Supplementary Table S3). The first threshold 15

was set at the statistical significant level for the p-value using Student's t-tests (p < 0.05). 16

The phosphorylation level of proteins downstream of mTOR such as 4EBP1, 4EBP2, 17

ULK1, RPS6 and CAD was significantly decreased after treatment with gedatolisib as 18




15

compared with DMSO, when the cutoff point was set to 1.7-fold (Fig. 2A). Moreover, 1

the phosphorylation of AKT target proteins which have the phospho-AKT substrate 2

motif (RXXS*/T*), were also down-regulated in H1048 cells treated with gedatolisib 3

(Fig. 2A). Thus, gedatolisib treatment inhibited both AKT and mTOR kinase activities 4

in H1048 cells under these experimental conditions. 5

To validate the phosphoproteomic data, we used western blot (WB) analysis to 6

again assess for gedatolisib inhibition of AKT activity, mTOR activity, or related 7

signaling molecules by using four gedatolisib-sensitive and five gedatolisib-resistant 8

cell lines. Amounts of total AKT, phospho (p)-AKT (T308), p-AKT (S473), total 4EBP1, 9

p-4EBP1, and GAPDH were determined in cells treated with DMSO or gedatolisib (100 10

nM) for 2 hrs. We noticed that phosphorylation of AKT at S473 by mTORC2 was 11

detected in all SCLC cell lines under normal growth conditions, but that p-T308 AKT, 12

which is phosphorylated by PDK1, was not detected in the two resistant cell lines, 13

DMS53 and SHP77 (Fig. 2B). Despite equivalent amounts of total AKT, p-T308 and 14

p-S473 AKT were clearly suppressed by gedatolisib in the four sensitive cell lines and 15

even in three of the five resistant cell lines tested (H69, H1092 and H1694 cells) (Fig. 16

2B). Phosphorylation of the downstream signaling molecule 4EBP1 was also inhibited 17

by the addition of gedatolisib to H1048, DMS114, H187, H446, H69, H1092, and 18




16

H1694 cells but not to the extremely resistant DMS53 and SHP77 cells (Fig. 2B). Thus, 1

treatment with gedatolisib inactivated the PI3K pathway in all four sensitive cell lines 2

but also in three resistant cell lines. Taking these findings together, we concluded that 3

protein phosphorylation status was not concordant with sensitivity to gedatolisib in 4

SCLC cells. 5

6

Comprehensive phosphatidyl lipid analysis revealed that a specific PIP3 subspecies 7

was a definitive lipid product to assess sensitivity to gedatolisib. 8

Class I PI3Ks function by phosphorylating the inositol ring of PIP2 to generate the 9

second messenger PIP3(12, 18). AKT activation is facilitated by PIP3, which is 10

dephosphorylated by the phosphatase PTEN, generating PIP2(10, 11, 18, 39). The 11

depletion of phosphatases such as PTEN leads to enhanced AKT activation, suggesting 12

that increased levels of PIP3 may be sufficient to activate AKT. Because AKT activation 13

was observed in SCLC cell lines (Fig. 2B), we hypothesized that the quantity of 14

phosphoinositide correlates with sensitivity to gedatolisib. To test this idea, we analyzed 15

the phosphatidyl lipid profile, including a variety of acyl groups, in cellular lipid 16

extracts using electrospray ionization (ESI) mass spectrometry(36). Total lipid extracts 17

including PIPs were prepared from four gedatolisib-sensitive and seven 18




17

gedatolisib-resistant SCLC cell lines. We were able to resolve and identify 17 PIP1, PIP2, 1

and PIP3 compounds as well as 4 PS compounds from H1048 cells extracted 6 hours 2

after treatment with gedatolisib. Although there were significant differences in total PIP3 3

levels across all cell lines tested, correlations between levels of total PIPs and 4

corresponding gedatolisib sensitivity across the 11 cell lines were not observed (Fig. 3A, 5

Supplementary Table S4). Previous studies for PIP species using mass spectrometry 6

analysis showed that the 36:2 and 38:4 acyl groups were most frequently detected in 7

human and mouse cells(36). To examine this further, we focused on PIPs consisting of 8

38:4 acyl groups in H1048 cells with and without gedatolisib treatment (Fig. 3B, 9

Supplementary Table S5). Despite equivalent levels of PIP1 (38:4) and PIP2 (38:4), PIP3 10

(38:4) was significantly decreased in cells after treatment with gedatolisib (Fig. 3B). We 11

hypothesized that the profile of PIP (38:4) may predict the sensitivity of SCLC cells to 12

gedatolisib with greater accuracy compared with total PIPs. To test this, we quantified 13

PIP (38:4) levels across the 11 SCLC cell lines. Although the level of PIP2 (38:4) was 14

equivalent in both sensitive and resistant cells, PIP3 (38:4) as a substrate of PI3K was 15

detected in gedatolisib-sensitive cell lines such as H1048 and DMS114 cells, but not in 16

resistant SCLC cell lines (Fig. 3C). Therefore, our data indicate that the amount of PIP3 17




18

(38:4) may be predictive for gedatolisib sensitivity in SCLC cells, and support the 1

notion that gedatolisib targets PI3K activity in sensitive SCLC cells. 2

3

Treatment with gedatolisib efficiently suppressed H1048 tumor growth and 4

induced apoptosis in a mouse xenograft model 5

To further investigate whether the PI3K/AKT/mTOR pathway could be a therapeutic 6

target in SCLC, we tested the in vivo drug sensitivity of the SCLC cell lines using 7

gedatolisib at a dosing interval targeting this pathway(22, 40). We used the nude mouse 8

xenograft model subcutaneously transplanted with sensitive H1048 or resistant SBC-5 9

cells to test gedatolisib efficacy against SCLC tumors in vivo (Fig. 4A and 4B). After 10

the tumors reached an average volume of 100 mm3, mice were treated with gedatolisib 11

or vehicle. Gedatolisib 10 mg/kg was administered to each mouse followed by 3 d of 12

rest, for a total of 6 injections over time(22, 40). Body-weight loss in all mice was not 13

detected after gedatolisib treatment. In this mouse model, H1048 cells treated with 14

vehicle control started to grow exponentially at 7 days post-transplant and the tumor 15

grew progressively for 4 weeks (Fig. 4A). Gedatolisib treatment dramatically 16

suppressed the growth of H1048 tumors, whereas growth of SBC-5 tumors was not 17

changed by gedatolisib treatment (Fig. 4A and 4B). These results confirm that H1048 18



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exhibits a gedatolisib-sensitive phenotype whereas SBC-5 has a resistant phenotype, 1

indicating that the in vivo efficacy of gedatolisib against SCLC cells is consistent with 2

its efficacy in vitro. The mean baseline PIP3 (38:4) level was higher in H1048 tumors 3

(0.227 p mol/mg (tissue)) than in SBC-5 tumors (0.038 p mol/mg (tissue)) 4

(Supplementary Fig. S1). 5

The mechanism of growth inhibition of SCLC tumor growth by gedatolisib was 6

further examined by immunohistochemistry (IHC). Representative IHC images of 7

gedatolisib-treated tumors revealed a dramatic change in SCLC pathology in the 8

H1048-derived tumors at 6 hours post gedatolisib treatment (Fig. 4C). Intravenous 9

injection of gedatolisib to H1048-xenografted mice induced significant apoptosis, as 10

revealed by cleaved poly ADP-ribose polymerase (cPARP) in the H1048-derived 11

tumors that was not observed in control mice administered the vehicle. In contrast, 12

cPARP was not increased in resistant SBC-5 tumors (Fig. 4C), indicating that 13

gedatolisib suppresses H1048 tumor growth through apoptotic mechanisms. Taken 14

together, these results prove that the PI3K pathway has an important role for SCLC 15

tumor growth and cell survival in vivo. 16

17

Metabolic changes in an in vitro or in vivo model by gedatolisib treatment 18




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We next aimed to assess which metabolic pathways were altered after inhibition of 1

PI3K in both sensitive and resistant SCLC cells, by measuring the levels of 116 2

metabolites in the presence or absence of gedatolisib across three independent 3

experiments. The ratios of metabolite levels observed for gedatolisib treatment and 4

DMSO control are shown in logarithmic scale (Supplementary Fig. S2A). Between 24 – 5

41 metabolites (p < 0.05) in the 4 sensitive SCLC cell lines were significantly altered 6

after treatment with gedatolisib (Supplementary Table S6). In the resistant H1694 SCLC 7

cells, a similar number of metabolites (33, p < 0.05) was altered with or without 8

treatment with gedatolisib (Supplementary Table S6). 9

Three metabolites phosphoribosyl diphosphate (PRPP), ADP-Ribose 10

(ADP-Rib), and lactate were decreased in the gedatolisib-sensitive cell lines but not in 11

the gedatolisib-resistant cell lines (Supplementary Fig. S2A). Despite equivalent levels 12

of ATP, the levels of AMP, carbamoyl-aspartate (CM-Asp) and putrescine were 13

decreased in both sensitive and resistant SCLC cells (Supplementary Fig. S2A). 14

Moreover, various amino acids were increased in both sensitive and resistant SCLC cell 15

lines after treatment with gedatolisib (Supplementary Fig. S2A). 16

To confirm the metabolic alterations observed in the in vitro experiments 17

(Supplementary Fig. S2A), the metabolic profile was analyzed in tumors derived from 18




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sensitive or resistant SCLC cells transplanted into nude mice with or without gedatolisib 1

treatment (Supplementary Table S7). The absolute amount of 116 extracted metabolites 2

was obtained in 3–5 independent experiments (H1048 cells: n = 5, SBC-5 cells: n = 3). 3

In H1048 xenografts sensitive to gedatolisib, 35 metabolites exhibited statistically 4

significant changes after treatment with gedatolisib (p < 0.05, Supplementary Table S7). 5

While in SBC-5 xenografts resistant to gedatolisib, 9 of 116 metabolites were altered 6

after gedatolisib treatment (p < 0.05, Supplementary Table S7). The level of PRPP was 7

decreased in the gedatolisib-sensitive, but not -resistant, SCLC xenografts 8

(Supplementary Fig. S2B). Putrescine was decreased in both sensitive and resistant 9

SCLC xenografts (Supplementary Fig. S2B). Several amino acids accumulated in both 10

sensitive and resistant SCLC xenografts (Supplementary Fig. S2B), consistent with the 11

in vitro experiments (Supplementary Fig. S2A). Taken together, the reduction in PRPP 12

levels and the accumulation of amino acids appear to be a characteristic metabolic 13

signature of SCLC after treatment with a PI3K/mTOR dual inhibitor. 14

15

Metabolic determinants of sensitivity to PI3K pathway inhibitor in SCLC 16

We generated a large-scale metabolomic data set for 28 SCLC cell lines, together with 17

the pharmacological IC50 of gedatolisib across 42 SCLC cell lines. To evaluate 18




22

differences in characteristic metabolites in SCLC cells and lung adenocarcinoma cells, 1

we compared metabolic profiles between 4 lung adenocarcinoma cell lines(35) and 28 2

SCLC cell lines, including resistant SCLC cell lines and classified them using partial 3

least squares discriminant analysis (PLS-DA). (Supplementary Fig. S3). As shown in 4

Supplementary Fig. S3, endogenous metabolites in the lung adenocarcinoma cells 5

clearly differed from those in the SCLC cells, indicating that SCLC cells have a 6

metabolic profile distinct from lung adenocarcinoma cells. The trajectory of the 7

sensitive SCLC cells was distinguishable from the position of the resistant SCLC cells 8

according to latent variable 2 (LV2) (Supplementary Fig. S3), suggesting that the 9

metabolic profile in sensitive SCLC cells is unique when compared to resistant SCLC 10

cells. 11

To identify a metabolic biomarker for the prediction of gedatolisib sensitivity, 12

we compared levels of various metabolites between sensitive and resistant SCLC cells 13

before gedatolisib treatment. Under normal growth conditions, the levels of metabolites 14

in central carbon metabolism were higher in the sensitive SCLC cell lines than in the 15

resistant SCLC cell lines (Fig. 5A). On the other hand, purine–related metabolites such 16

as HXT, AMP, and GMP were significantly lower (p < 0.05) in sensitive than in 17

resistant SCLC cell lines (Fig. 5A). Another purine–related metabolites: 18




23

adenyl-succinate (Ad-SA), XMP, IMP, guanine (GUA), and NADP+ were marginally 1

significantly lower (p < 0.1) in sensitive than in resistant SCLC cell lines (Fig. 5A). 2

While the amount of UA was high in sensitive SCLC cells, its precursor, HXT, was 3

present at low levels in these cells, although HXT levels were relatively higher in the 4

resistant SCLC cells (Fig. 5A). To identify significantly enriched metabolic pathways 5

related to gedatolisib sensitivity in SCLC cells, we performed metabolite set enrichment 6

analysis (MSEA). Purine metabolism was significantly altered in sensitive compared 7

with resistant SCLC cell lines (p < 0.001, q value < 0.01) (Fig. 5B, Supplementary 8

Table S8). 9

To reveal which metabolic pathway was important in determining sensitivity to 10

gedatolisib, we analyzed gene expression with or without gedatolisib treatment in 11

sensitive H1048 cells and resistant H1694 cells. RNA sequence analysis showed that the 12

expression level of genes involved in the Forkhead transcription factor (FOXO) 13

pathway was increased at 3 hours after treatment with gedatolisib in H1048 cells as 14

compared to H1694 cells (Supplementary Fig. S4A), consistent with previous 15

findings(41). Under this experimental condition, genes related to the methionine cycle 16

and the polyamine biosynthesis pathway were down-regulated in H1048 cells after 17

treatment with gedatolisib (Supplementary Fig. S4A). MAT and AMD1 are involved in 18




24

the methionine cycle and SRM has an important role in spermidine and 1

methylthioadenosine (MTA) biosynthesis in the purine salvage pathway (Supplementary 2

Fig. S5). To confirm the RNA sequence data, we measured AMD1, SRM, and MAT2A 3

mRNA level using the reverse transcription-polymerase chain reaction (RT-PCR). The 4

relative levels of AMD1, SRM and MAT2A were significantly down-regulated in 5

gedatolisib-treated H1048 cells, but were not changed in resistant H1694 cells 6

(Supplementary Fig. S4B). In addition, the expression of AMD1 was significantly 7

decreased in the sensitive DMS114 cells after treatment (Supplementary Fig. S4B). 8

Taken together, these findings showed that the expression of enzymes involved in 9

polyamine biosynthesis and the methionine cycle was decreased in gedatolisib-sensitive 10

SCLC cells after inhibition of the PI3K pathway. 11

The purine salvage pathway is linked with the methionine cycle and polyamine 12

biosynthesis pathways (Supplementary Fig. S5). Gedatolisib treatment significantly 13

decreased the levels of PRPP in gedatolisib-sensitive but not gedatolisib-resistant cell 14

lines (Supplementary Fig. S2A-B). Those results led us to hypothesize that the addition 15

of MTA changed the gedatolisib sensitivity of SCLC cells. To test this, we added MTA 16

to the culture medium and measured survival of the H1048 and DMS114 sensitive cell 17

lines after treatment with 10 or 100 nM of gedatolisib. Although purine metabolites 18




25

were supplied through either the de novo or salvage pathway, we also considered the 1

possibility that the purine salvage pathway might be down-regulated during purine 2

biosynthesis after gedatolisib treatment. As predicted, the addition of MTA to the culture 3

media of two drug-sensitive cell lines, H1048 and DMS114, resulted in a more 4

gedatolisib-resistant phenotype, as reflected by increased cell survival (Fig. 5C). In 5

contrast, the addition of MTA to the culture media of the drug-sensitive cell lines 6

DMS273 and SBC-5 resulted in a more gedatolisib-sensitive phenotype (Supplementary 7

Fig. S6A-B). These results indicate that the vulnerability to targeting of the PI3K 8

pathway in SCLC cells could be purine biosynthesis pathway. 9

Together, these results suggest a potential molecular mechanism through which 10

gedatolisib treatment down-regulates the de novo purine biosynthesis pathway. Even 11

though gedatolisib inhibits the de novo purine biosynthetic pathway, the purine salvage 12

pathway also supplies purine resources required for the biosynthesis of purine 13

nucleotides (Fig. 6A). To verify this idea, we focused on the purine salvage pathway, 14

because the characteristic metabolic feature of gedatolisib-resistant SCLC cells was the 15

high levels of hypoxanthine (Fig. 6A). Notably, the levels of the mRNA encoding 16

hypoxanthine phosphoribosyl transferase 1 (HPRT1), a key component of the purine 17

salvage pathway, differed significantly between cells sensitive or resistant to gedatolisib 18




26

(p = 0.00311) (Fig. 6B). We thought that the high level of HPRT1 expression might 1

cause drug resistance of SCLC cells. To test this idea, we measured the cellular 2

sensitivity to gedatolisib after introduction of siRNAs including non-targeting control 3

(NC) or three sets of HPRT1 sequences. siRNA targeting HPRT1 successfully 4

knocked-down HPRT1 mRNA and HPRT1 in DMS273 cells compared with the 5

negative control expressing siNC (Supplementary Fig. S7A-B). Surprisingly, loss of 6

HPRT1 in SCLC cells decreased cellular proliferation in the presence of gedatolisib 7

compared with the non-targeting control (Fig. 6C). Further, the level of HPRT1 was 8

higher in the gedatolisib-resistant tumors than the sensitive tumors (Supplementary Fig. 9

S7C). These results indicate that the resistance mechanism of PI3K pathway inhibitors 10

is mediated by the activation of the purine salvage pathway, supplying purine resource 11

to nucleotide biosynthesis independent of de novo purine biosynthesis (Fig. 6A). 12

13

Metabolic profiles of clinical samples of SCLC tissues 14

To evaluate SCLC-specific metabolism, we compared the levels of 116 hydrophilic 15

metabolites using liquid chromatography coupled with tandem mass spectrometry 16

(LC-MS/MS) methods from training 4 matching pairs of high-quality surgically 17

resected fresh and frozen SCLC tumors and adjacent non-tumor tissues (same number 18




27

as training set). We further analyzed the levels of these 116 metabolites in an 1

independent test set of 17 samples that were obtained from 13 SCLC tumors (two 2

surgically resected and 11 taken from biopsies of patients with advanced disease) and 3

unpaired four non-tumor tissues (same number as the testing set) (Supplementary Table 4

S9). We initially performed PCA of the 116 metabolites obtained from each sample to 5

discern the presence of inherent similarities in SCLC metabolic profiles. Five 6

metabolites were not detected in any cases (Supplementary Table S9). Statistically 7

significant differences between SCLC tumor tissues and adjacent non-tumor tissues 8

were observed in 46 metabolites, and the relative levels of these metabolites in tumor vs. 9

non-tumor tissue were plotted (Fig. 7). It was characteristic that purine-related 10

metabolites, such as hypoxanthine, were higher in SCLC tumor tissues. Purine-related 11

metabolites are characteristic of SCLC biology, which might serve as novel therapeutic 12

biomarkers such as the PI3K pathway. 13

14

DISCUSSION 15

Our studies shown in this paper revealed that higher levels of purine pathway-related 16

metabolites correlating with SCLC sensitivity to a PI3K pathway inhibitor. This shows 17

that sensitivity to gedatolisib is determined by cellular metabolic characteristics, 18




28

including both hydrophilic metabolites. We also showed that complementation with 1

purine metabolites change the sensitivity to gedatolisib from sensitive SCLC cells to 2

resistant phenotype. The purine salvage pathway, which might be activated in 3

gedatolisib-resistant cells, was associated with resistance to the PI3K pathway inhibitor, 4

which was indicated by the high levels of HPRT1 mRNA in resistant cells. Finally, our 5

results indicated that the level of purine-related metabolites can be potential biomarkers 6

for a response to inhibitors targeting the PI3K/AKT/mTOR pathway. 7

We demonstrated that treatment with gedatolisib effectively repressed the 8

growth of H1048 cells harboring active PIK3CA mutations such as H1047R in the 9

kinase domain. Gedatolisib inhibits PI3K, mTOR, and its downstream signaling 10

molecules in SCLC cells (Fig. 2B) and induces apoptosis in H1048 tumor xenografts 11

(Fig. 4C). It is still unclear how the genomic profiles affect signaling through the PI3K 12

pathway. Indeed, half of the gedatolisib-sensitive cell lines did not harbor genomic 13

alterations in the PI3K/AKT/mTOR pathway (Fig. 1A). Since this population cannot be 14

detected by genomic screening alone, the provision of targeted therapies to SCLC 15

patients requires more feasible biomarkers of sensitive populations without omission. 16

Metabolomics is considered to be more representative to the actual phenotype compared 17

with proteomics or genomics data(42). Although stathmin, a microtubule regulatory 18




29

protein associated with HIF-1 alpha levels, was used as a biomarker for gedatolisib in a 1

phase II study of patients with recurrent endometrial cancer, high stathmin expression 2

did not correlate with greater treatment efficacy(24). A proteomic approach to explore 3

biomarkers of PI3K inhibitors was unsuccessful in this study; however changes in 4

glucose homeostasis consistent with a PI3K blockade were observed following 5

administration of gedatolisib, suggesting the usefulness of metabolic biomarkers. For 6

reasons such as these, a metabolomic approach for discovering novel therapeutic 7

biomarkers of this pathway is feasible. 8

The activated PI3K and mTOR pathway induces vigorous cellular metabolism 9

to build up macromolecules in response to growth signals in a variety of cancers(14, 17, 10

31). We have previously shown that mutant EGFR signaling in lung adenocarcinoma 11

cells maintained up-regulated aerobic glycolysis, PPP, and de novo pyrimidine 12

biosynthesis through the PI3K pathway(15, 35). Here we show that sensitivity to 13

gedatolisib is determined by the global metabolic profile, including both hydrophobic 14

and hydrophilic metabolite composition. Specifically, the level of PIP3 38:4 was 15

different between gedatolisib-sensitive and gedatolisib-resistant SCLC (Fig. 3C). It is 16

reported that the elevated level of PIP3 is related to PI3K/AKT/mTOR pathway 17

activation(36). Our results suggest that PIP3 38:4 is crucial to activation of the PI3K 18




30

pathway, suggesting its utility as an indicator for predicting sensitivity to PI3K pathway 1

inhibitors such as gedatolisib. PIP3 (38:4) levels were evaluable in xenograft tumors. 2

Direct measurement of PIP3 levels in a recent study revealed an unexpected role of PI3K 3

in luminal breast cancer(39). Although the ELISA method does not allow for the 4

determination of molecular species, we could identify it by analyzing the PIP profile 5

using ESI mass spectrometry. ESI mass spectrometry might become a more specific and 6

promising method for future biomarker analysis. 7

We showed that high levels of purine-related metabolites allowed SCLC cells 8

to become resistant to the PI3K pathway inhibitor. Purines, including adenine 9

nucleotides, guanine nucleotides, cAMP, NAD, HXT and xanthine, are a class of small 10

organic molecules that are essential for all cells. These molecules play central roles as 11

building blocks for DNA and RNA during cell proliferation, serve as cofactors for many 12

enzymatic reactions, and act as both intracellular and intercellular signaling 13

messengers(43). Purine metabolism is linked to the PI3K pathway, which regulates the 14

early steps of de novo synthesis by modulating PRPP production and the activity of 15

enzymes such as IMP cyclohydrolase and transketolase(44, 45). It has been shown that 16

mTORC1 induces de novo purine biosynthesis through the transcriptional factor 17

ATF4(46). The methionine cycle and polyamine biosynthesis pathway links to the 18




31

salvage pathway through MTA biosynthesis(47, 48). Gedatolisib reduced putrescine and 1

PRPP in vitro and in vivo in sensitive H1048 cells (Supplementary Fig. S2A-B). This is 2

likely due to the decreased purine salvage pathway in gedatolisib-sensitive cells. In that 3

case, drug sensitivity to gedatolisib would be altered if an exogenous purine precursor 4

compound were added. Indeed, adding MTA induced a more resistant phenotype in the 5

gedatolisib-sensitive SCLC cells (Fig. 5C). It is possible that gedatolisib treatment 6

downregulates the purine salvage pathway during purine biosynthesis, although purine 7

metabolites can be supplied through either the de novo or salvage pathways. 8

Recent studies have indicated that MTA metabolism is an important metabolic 9

feature in cancer cells(49, 50). We demonstrated that SCLC cells accumulate high levels 10

of purine-related metabolites, and a higher amount of purine-related metabolites 11

correlates negatively with their sensitivity to gedatolisib. PI3K/AKT/mTOR signaling 12

regulates the central carbon metabolism of cancer cells in an AKT-dependent or 13

-independent manner(15, 51). Here we show that gedatolisib treatment dramatically 14

decreased the levels of PRPP in drug-sensitive cell lines but not those in drug-resistant 15

cell lines (Supplementary Fig. S2A). Specifically, the levels of the intermediate 16

metabolites of the central carbon metabolism pathway were higher in 17

gedatolisib-sensitive cells compared with those in resistant cells (Fig. 5A). In contrast, 18




32

the basal levels of purine-related metabolites were lower in gedatolisib-sensitive cells 1

compared with those of resistant cells (Fig. 5A). 2

Moreover, complementation with purine metabolites reversed vulnerability to 3

targeting the PI3K pathway in SCLC cells, which were normally sensitive to gedatolisib 4

(Fig. 5C). Further, we found that a characteristic metabolic feature of the 5

gedatolisib-resistant SCLC cells was their high levels of hypoxanthine and the mRNA 6

encoding HPRT1. These levels differed significantly between gedatolisib-sensitive and 7

-resistant cells (Fig. 6B). Therefore, we suggest that the resistance mechanism of PI3K 8

pathway inhibitors is the activation of the purine salvage pathway, which also supplies 9

purine resources required for the nucleotide biosynthesis. 10

SCLC is characterized as a tumor with cells that have a small size, a 11

round-to-fusiform shape, scant cytoplasm, and finely granular nuclear chromatin. In 12

addition, mitotic rates are high, with an average of 80 mitoses per 2-mm2 area(52). This 13

means that nucleic acid synthesis is especially active in SCLC. Purine metabolism, 14

which plays a pivotal role in nucleic acid synthesis, might be the metabolic pathway 15

which captures the characteristic feature of SCLC biology (Fig. 7). Accordingly, 16

measurement of the level of purine-related metabolites is important not only to identify 17

potential biomarkers of PI3K/mTOR pathway inhibitors but also for exploring other 18




33

novel therapeutic strategies. Further research to investigate the molecular mechanisms 1

that regulate purine metabolism in SCLC cells is warranted. Our findings indicate that 2

the purine-related metabolic pathway is a vulnerability of SCLC, thus this pathway 3

could be promising for future therapeutic targets. 4

5

Acknowledgements 6

We wish to thank Drs. Shogo Nomura, Keisuke Kirita, Kiyotaka Yoh, Phil Wong and 7

Hironobu Ohmatsu for their support and comments. We wish to thank HMT, ALTe and 8

TechnoPro for professional assistance. This study was performed as a research program 9

of the Project for Development of Innovative Research on Cancer Therapeutics 10

(P-Direct). This study was supported by Ministry of Education, Culture, Sports, Science 11

and Technology, Japan, Ministry of Health, Labour and Welfare, Japan, Japan Agency 12

for Medical Research and Development (AMED), the National Cancer Center Research 13

and Development Fund (28-A-9) and funds from Yamagata Prefecture and Tsuruoka 14

City. 15

16

17

18




34

References 1

1. Rekhtman N. Neuroendocrine tumors of the lung: an update. Arch Pathol Lab Med. 2

2010;134:1628-38. 3

2. Byers LA, Rudin CM. Small cell lung cancer: where do we go from here? Cancer. 4

2015;121:664-72. 5

3. William WN, Jr., Glisson BS. Novel strategies for the treatment of small-cell lung carcinoma. Nat 6

Rev Clin Oncol. 2011;8:611-9. 7

4. Pietanza MC, Byers LA, Minna JD, Rudin CM. Small cell lung cancer: will recent progress lead 8

to improved outcomes? Clin Cancer Res. 2015;21:2244-55. 9

5. Bunn PA, Jr., Minna JD, Augustyn A, Gazdar AF, Ouadah Y, Krasnow MA, et al. Small Cell Lung 10

Cancer: Can Recent Advances in Biology and Molecular Biology Be Translated into Improved 11

Outcomes? J Thorac Oncol. 2016;11:453-74. 12

6. Rudin CM, Durinck S, Stawiski EW, Poirier JT, Modrusan Z, Shames DS, et al. Comprehensive 13

genomic analysis identifies SOX2 as a frequently amplified gene in small-cell lung cancer. Nat 14

Genet. 2012;44:1111-6. 15

7. Peifer M, Fernandez-Cuesta L, Sos ML, George J, Seidel D, Kasper LH, et al. Integrative genome 16

analyses identify key somatic driver mutations of small-cell lung cancer. Nat Genet. 17

2012;44:1104-10. 18

8. George J, Lim JS, Jang SJ, Cun Y, Ozretic L, Kong G, et al. Comprehensive genomic profiles of 19

small cell lung cancer. Nature. 2015;524:47-53. 20

9. Dowlati A, Lipka MB, McColl K, Dabir S, Behtaj M, Kresak A, et al. Clinical correlation of 21

extensive-stage small-cell lung cancer genomics. Ann Oncol. 2016;27:642-7. 22

10. Liu P, Cheng H, Roberts TM, Zhao JJ. Targeting the phosphoinositide 3-kinase pathway in 23

cancer. Nat Rev Drug Discov. 2009;8:627-44. 24

11. Engelman JA. Targeting PI3K signalling in cancer: opportunities, challenges and 25

limitations. Nat Rev Cancer. 2009;9:550-62. 26

12. Thorpe LM, Yuzugullu H, Zhao JJ. PI3K in cancer: divergent roles of isoforms, modes of 27

activation and therapeutic targeting. Nat Rev Cancer. 2015;15:7-24. 28

13. Dibble CC, Cantley LC. Regulation of mTORC1 by PI3K signaling. Trends Cell Biol. 29

2015;25:545-55. 30

14. Engelman JA, Luo J, Cantley LC. The evolution of phosphatidylinositol 3-kinases as 31

regulators of growth and metabolism. Nat Rev Genet. 2006;7:606-19. 32

15. Makinoshima H, Takita M, Saruwatari K, Umemura S, Obata Y, Ishii G, et al. Signaling 33

through the Phosphatidylinositol 3-Kinase (PI3K)/Mammalian Target of Rapamycin (mTOR) 34

Axis Is Responsible for Aerobic Glycolysis mediated by Glucose Transporter in Epidermal 35




35

Growth Factor Receptor (EGFR)-mutated Lung Adenocarcinoma. J Biol Chem. 1

2015;290:17495-504. 2

16. Pusapati RV, Daemen A, Wilson C, Sandoval W, Gao M, Haley B, et al. 3

mTORC1-Dependent Metabolic Reprogramming Underlies Escape from Glycolysis Addiction in 4

Cancer Cells. Cancer Cell. 2016;29:548-62. 5

17. Lien EC, Lyssiotis CA, Cantley LC. Metabolic Reprogramming by the PI3K-Akt-mTOR 6

Pathway in Cancer. Recent Results Cancer Res. 2016;207:39-72. 7

18. Sasaki T, Takasuga S, Sasaki J, Kofuji S, Eguchi S, Yamazaki M, et al. Mammalian 8

phosphoinositide kinases and phosphatases. Prog Lipid Res. 2009;48:307-43. 9

19. McFadden DG, Papagiannakopoulos T, Taylor-Weiner A, Stewart C, Carter SL, Cibulskis 10

K, et al. Genetic and clonal dissection of murine small cell lung carcinoma progression by 11

genome sequencing. Cell. 2014;156:1298-311. 12

20. Umemura S, Mimaki S, Makinoshima H, Tada S, Ishii G, Ohmatsu H, et al. Therapeutic 13

priority of the PI3K/AKT/mTOR pathway in small cell lung cancers as revealed by a 14

comprehensive genomic analysis. J Thorac Oncol. 2014;9:1324-31. 15

21. Umemura S, Tsuchihara K, Goto K. Genomic profiling of small-cell lung cancer: the era of 16

targeted therapies. Jpn J Clin Oncol. 2015;45:513-9. 17

22. Mallon R, Feldberg LR, Lucas J, Chaudhary I, Dehnhardt C, Santos ED, et al. Antitumor 18

efficacy of PKI-587, a highly potent dual PI3K/mTOR kinase inhibitor. Clin Cancer Res. 19

2011;17:3193-203. 20

23. Shapiro GI, Bell-McGuinn KM, Molina JR, Bendell J, Spicer J, Kwak EL, et al. 21

First-in-Human Study of PF-05212384 (PKI-587), a Small-Molecule, Intravenous, Dual Inhibitor 22

of PI3K and mTOR in Patients with Advanced Cancer. Clin Cancer Res. 2015;21:1888-95. 23

24. Del Campo JM, Birrer M, Davis C, Fujiwara K, Gollerkeri A, Gore M, et al. A randomized 24

phase II non-comparative study of PF-04691502 and gedatolisib (PF-05212384) in patients with 25

recurrent endometrial cancer. Gynecologic oncology. 2016;142:62-9. 26

25. Wishart DS. Emerging applications of metabolomics in drug discovery and precision 27

medicine. Nat Rev Drug Discov. 2016;15:473-84. 28

26. Hakimi AA, Reznik E, Lee CH, Creighton CJ, Brannon AR, Luna A, et al. An Integrated 29

Metabolic Atlas of Clear Cell Renal Cell Carcinoma. Cancer Cell. 2016;29:104-16. 30

27. Kami K, Fujimori T, Sato H, Sato M, Yamamoto H, Ohashi Y, et al. Metabolomic profiling 31

of lung and prostate tumor tissues by capillary electrophoresis time-of-flight mass spectrometry. 32

Metabolomics. 2013;9:444-53. 33

28. Cairns RA, Harris IS, Mak TW. Regulation of cancer cell metabolism. Nat Rev Cancer. 34

2011;11:85-95. 35




36

29. Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 1

2011;144:646-74. 2

30. DeBerardinis RJ, Lum JJ, Hatzivassiliou G, Thompson CB. The biology of cancer: 3

metabolic reprogramming fuels cell growth and proliferation. Cell Metab. 2008;7:11-20. 4

31. Vander Heiden MG, Cantley LC, Thompson CB. Understanding the Warburg effect: the 5

metabolic requirements of cell proliferation. Science. 2009;324:1029-33. 6

32. Cheong H, Lu C, Lindsten T, Thompson CB. Therapeutic targets in cancer cell metabolism 7

and autophagy. Nat Biotechnol. 2012;30:671-8. 8

33. Boroughs LK, DeBerardinis RJ. Metabolic pathways promoting cancer cell survival and 9

growth. Nat Cell Biol. 2015;17:351-9. 10

34. Sullivan LB, Gui DY, Heiden MG. Altered metabolite levels in cancer: implications for 11

tumour biology and cancer therapy. Nat Rev Cancer. 2016. 12

35. Makinoshima H, Takita M, Matsumoto S, Yagishita A, Owada S, Esumi H, et al. 13

Epidermal growth factor receptor (EGFR) signaling regulates global metabolic pathways in 14

EGFR-mutated lung adenocarcinoma. J Biol Chem. 2014;289:20813-23. 15

36. Kofuji S, Kimura H, Nakanishi H, Nanjo H, Takasuga S, Liu H, et al. INPP4B Is a 16

PtdIns(3,4,5)P3 Phosphatase That Can Act as a Tumor Suppressor. Cancer Discov. 2015;5:730-9. 17

37. Clark J, Anderson KE, Juvin V, Smith TS, Karpe F, Wakelam MJ, et al. Quantification of 18

PtdInsP3 molecular species in cells and tissues by mass spectrometry. Nat Methods. 19

2011;8:267-72. 20

38. Yamamoto H, Fujimori T, Sato H, Ishikawa G, Kami K, Ohashi Y. Statistical hypothesis 21

testing of factor loading in principal component analysis and its application to metabolite set 22

enrichment analysis. BMC bioinformatics. 2014;15:51. 23

39. Costa C, Ebi H, Martini M, Beausoleil SA, Faber AC, Jakubik CT, et al. Measurement of 24

PIP3 levels reveals an unexpected role for p110beta in early adaptive responses to 25

p110alpha-specific inhibitors in luminal breast cancer. Cancer Cell. 2015;27:97-108. 26

40. Venkatesan AM, Dehnhardt CM, Delos Santos E, Chen Z, Dos Santos O, Ayral-Kaloustian 27

S, et al. Bis(morpholino-1,3,5-triazine) derivatives: potent adenosine 5'-triphosphate competitive 28

phosphatidylinositol-3-kinase/mammalian target of rapamycin inhibitors: discovery of compound 29

26 (PKI-587), a highly efficacious dual inhibitor. J Med Chem. 2010;53:2636-45. 30

41. Hill R, Kalathur RK, Callejas S, Colaco L, Brandao R, Serelde B, et al. A novel 31

phosphatidylinositol 3-kinase (PI3K) inhibitor directs a potent FOXO-dependent, 32

p53-independent cell cycle arrest phenotype characterized by the differential induction of a subset 33

of FOXO-regulated genes. Breast Cancer Res. 2014;16:482. 34

42. Kovac JR, Pastuszak AW, Lamb DJ. The use of genomics, proteomics, and metabolomics 35

in identifying biomarkers of male infertility. Fertility and sterility. 2013;99:998-1007. 36




37

43. Balasubramaniam S, Duley JA, Christodoulou J. Inborn errors of purine metabolism: 1

clinical update and therapies. J Inherit Metab Dis. 2014;37:669-86. 2

44. Wang W, Fridman A, Blackledge W, Connelly S, Wilson IA, Pilz RB, et al. The 3

phosphatidylinositol 3-kinase/akt cassette regulates purine nucleotide synthesis. J Biol Chem. 4

2009;284:3521-8. 5

45. Saha A, Connelly S, Jiang J, Zhuang S, Amador DT, Phan T, et al. Akt phosphorylation 6

and regulation of transketolase is a nodal point for amino acid control of purine synthesis. Mol 7

Cell. 2014;55:264-76. 8

46. Ben-Sahra I, Hoxhaj G, Ricoult SJ, Asara JM, Manning BD. mTORC1 induces purine 9

synthesis through control of the mitochondrial tetrahydrofolate cycle. Science. 2016;351:728-33. 10

47. Gerner EW, Meyskens FL, Jr. Polyamines and cancer: old molecules, new understanding. 11

Nat Rev Cancer. 2004;4:781-92. 12

48. Witherspoon M, Chen Q, Kopelovich L, Gross SS, Lipkin SM. Unbiased metabolite 13

profiling indicates that a diminished thymidine pool is the underlying mechanism of colon cancer 14

chemoprevention by alpha-difluoromethylornithine. Cancer Discov. 2013;3:1072-81. 15

49. Mavrakis KJ, McDonald ER, 3rd, Schlabach MR, Billy E, Hoffman GR, deWeck A, et al. 16

Disordered methionine metabolism in MTAP/CDKN2A-deleted cancers leads to dependence on 17

PRMT5. Science. 2016;351:1208-13. 18

50. Kryukov GV, Wilson FH, Ruth JR, Paulk J, Tsherniak A, Marlow SE, et al. MTAP deletion 19

confers enhanced dependency on the PRMT5 arginine methyltransferase in cancer cells. Science. 20

2016;351:1214-8. 21

51. Hu H, Juvekar A, Lyssiotis CA, Lien EC, Albeck JG, Oh D, et al. Phosphoinositide 22

3-Kinase Regulates Glycolysis through Mobilization of Aldolase from the Actin Cytoskeleton. 23

Cell. 2016;164:433-46. 24

52. Travis WD. Update on small cell carcinoma and its differentiation from squamous cell 25

carcinoma and other non-small cell carcinomas. Mod Pathol. 2012;25 Suppl 1:S18-30. 26

27

28

29

30

31

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Figure Legends 1

Figure 1: Classification of SCLC cell lines as gedatolisib-sensitive and -resistant 2

(A and B) Sensitivity to gedatolisib was tested on 42 SCLC cell lines in cell 3

proliferation assays, and IC50 values were determined. Proliferative effects on SCLC 4

cell lines were plotted as IC50 following exposure to (A) gedatolisib (1–1000 nM) and 5

(B) etoposide (1-10,000 nM). Proliferation assays (n = 6) were conducted in duplicate 6

and IC50 values were calculated from the mean. The hierarchical cluster analysis 7

generated a cluster of four gedatolisib-sensitive cell lines. The names of sensitive cell 8

lines are in red and resistant cell lines are in blue (A, p <0.001, Mann-Whitney U test). 9

For SCLC cell lines possessing genetic alterations, the genetic profiles of the PIK3CA, 10

other PI3K pathway, KRAS, and MYC family are in colored boxes. 11

12

Figure 2: Gedatolisib treatment changes phosphorylation status in the proteins 13

involved in PI3K pathway 14

(A) Quantitative phosphoproteomics was carried out and relative abundance of 15

representative phospho-peptides is shown (threshold, 1.7-fold). Student’s t tests were 16

used to calculate which phospho-peptides were significantly decreased or increased in 17




39

H1048 cells (n = 3) treated with gedatolisib (100 nM, 2 hours), relative to DMSO 1

control (p < 0.05). 2

(B) Phosphorylation status involving PI3K pathway proteins in gedatolisib-sensitive and 3

gedatolisib-resistant SCLC cell lines treated with or without gedatolisib. SCLC cells 4

were treated with DMSO or gedatolisib (100 nM) for 2 hrs, and whole-cell lysates were 5

analyzed by western blotting with antibodies to described proteins. GAPDH served as a 6

loading control. 7

8

Figure 3: Comprehensive phosphatidyl lipid analysis revealing that a specific PIP3 9

subspecies is predictive to assess sensitivity to gedatolisib. 10

(A) Levels of total PIP2 and PIP3 per mg protein in SCLC cell lines are shown. Red bars 11

indicate gedatolisib-sensitive SCLC cell lines, and blue bars are gedatolisib-resistant. 12

(B) Levels of PIP1, PIP2 and PIP3 consisting of 38:4 acyl groups (38:4) in H1048 cells 13

for 6 hours with or without gedatolisib treatment were quantified and normalized to 14

total protein. Blue bars are the DMSO control and red bars indicate gedatolisib 15

treatment. Lipid extraction was done in triplicate (n = 3). Error bars represent one 16

standard deviation (s.d.), t-test. 17




40

(C) Levels of PIP2 (38:4) and PIP3 (38:4) per mg protein in SCLC cell lines. Red bars 1

indicate gedatolisib-sensitive SCLC cell lines and blue bars are gedatolisib-resistant. 2

* below the level of detection. 3

4

Figure 4: Gedatolisib-suppressed tumor growth in an SCLC xenograft mouse 5

model. 6

(A and B) SCLC cell lines (A) H1048 and (B) SBC-5 were inoculated into the 7

subcutaneous tissues of nude mice (10 mice per group). When the tumor volume 8

reached ~100 mm3, mice were intravenously administered gedatolisib (10 mg/kg, red 9

square) or vehicle control (blue circle) every 4 days for a total of six doses. Arrows 10

represent the days of gedatolisib injection. Tumor volume was measured at each time 11

point for 25–28 days. The number of evaluated tumors were H1048 vehicle (n = 17), 12

H1048 gedatolisib (n = 16), SBC-5 vehicle (n = 19) and SBC-5 gedatolisib (n = 21). 13

Error bars represent s.d. *p < 0.05, **p < 0.01, t-test. 14

(C) Representative images from immunohistochemistry using cleaved poly ADP-ribose 15

polymerase (PARP) antibody. Tumors were removed 6 hours after a single dose of 16

gedatolisib. Brown, apoptotic nuclei (white arrows) were visualized using anti-cleaved 17

PARP antibody and 3-3′-diaminobenzidine. Scale bar indicates 50 μm. 18




41

1

Figure 5 2

(A) Relative abundance of key metabolites in gedatolisib-sensitive SCLC cells, 3

normalized to gedatolisib-resistant SCLC cells. Mann-Whitney U tests were used to 4

calculate which metabolites were significantly lower or higher in gedatolisib-sensitive 5

SCLC cells, relative to gedatolisib-resistant SCLC cells (p < 0.05). Red dots indicate 6

purine-related metabolites, green dots indicate amino acids, and blue dots indicate 7

intermediate metabolites in the central carbon metabolism. 8

(B) Pie chart representing the results of metabolite set enrichment analysis (MSEA) to 9

identify significantly enriched metabolic pathways related to the sensitivity of SCLC 10

cells to gedatolisib. The levels of metabolites that were significantly lower (0.5-fold) or 11

higher (2-fold) in gedatolisib-sensitive compared with those of resistant SCLC cells 12

were selected. The number of metabolites selected in each metabolic pathway is shown. 13

Fisher’s exact test was used to evaluate the statistical significance of the enrichment of a 14

pathway (p < 0.05). 15

(C) Addition of 5'-deoxy-5'-(methylthio)-adenosine (MTA) changed drug sensitivity 16

from a sensitive to resistant phenotype. H1048 or DMS114 cell survival (%) was 17

measured in the presence of 0.1 mM MTA co-treated with either 100 nM or 10 nM of 18




42

gedatolisib. The data are shown as the mean ± s.d. (n = 6). Error bars represent one s.d. 1

**p < 0.01, ***p < 0.001, t-test. 2

3

Figure 6 4

(A) Diagram depicting a potential mechanism by which the treatment of gedatolisib 5

down-regulates de novo purine biosynthetic pathway by inhibiting the PI3K pathway, 6

and the purine salvage pathway also supplies purine resources required to biosynthesize 7

purine nucleotides. 8

(B) Levels of HPRT1 mRNAs in gedatolisib-sensitive (red bars) and -resistant cells 9

(blue bars) differed significantly. The data are shown as the mean ± s.d. (n=3). Error 10

bars represent one s.d. p = 0.00311, t test. 11

(C) Involvement of HPRT1 in gedatolisib sensitivity. DMS273 cells transfected with 12

HPRT1 siRNAs were incubated for 2 days, and cell survival was measured in the 13

presence of gedatolisib, 100 nM (left) or 10 nM (right) 72 h after treatment. The data are 14

shown as the mean ± S.D. (n = 4). *p < 0.05; **p < 0.01 (Student t test). 15

16

Figure 7: Metabolic profile in SCLC tumors 17




43

Relative abundance of key 116 metabolites in SCLC tumors (n = 17), normalized to 1

non-tumor tissues (n=8), was analyzed. Mann-Whitney U tests were used to calculate 2

which metabolites were significantly high or low in SCLC tumors, relative to adjacent 3

non-tumor tissues (p-value < 0.05). The metabolites, which were statistically significant, 4

are shown as a dot plot. Each dot presents a metabolite plotted as a function of fold 5

change (log 2, y-axis). Red dots indicate purine-related metabolites, green dots indicate 6

amino acids and blue dots indicate intermediate metabolites in the central carbon 7

metabolism. 8

9




Figure 1

NC

I-H

104

8

DM

S11

4

NC

I-H

187

N

CI-

H4

46

SBC

-3

NC

I-H

526

N

CI-

H2

11

Lu-1

39

N

CI-

H1

417

N

CI-

H2

196

N

CI-

H2

09

NC

I-H

82

SB

C-5

N

CI-

H1

882

D

MS2

73

ST

C1

N

CI-

H8

41

NC

I-H

69

N

CI-

H1

930

N

CI-

H1

46

NC

I-H

524

Lu

-13

5

NC

I-H

202

9

Lu-1

34

A

NC

I-H

217

1

NC

I-H

222

7

NC

I-H

110

5

NC

I-H

214

1

NC

I-H

109

2

NC

I-H

187

6

Lu-1

65

N

CI-

H5

10A

Lu

-14

3

NC

I-H

196

3

NC

I-H

208

1

NC

I-H

196

N

CI-

H1

694

D

MS7

9

RER

F-LC

-FM

M

S-1

-L

D

MS5

3

SHP

77

A

B

Other PI3K KRAS

MYC family

p = 0.001

NC

I-H

104

8

DM

S11

4

NC

I-H

187

N

CI-

H4

46

SBC

-3

NC

I-H

526

N

CI-

H2

11

Lu-1

39

N

CI-

H1

417

N

CI-

H2

196

N

CI-

H2

09

NC

I-H

82

SB

C-5

N

CI-

H1

882

D

MS2

73

ST

C1

N

CI-

H8

41

NC

I-H

69

N

CI-

H1

930

N

CI-

H1

46

NC

I-H

524

Lu

-13

5

NC

I-H

202

9

Lu-1

34

A

NC

I-H

217

1

NC

I-H

222

7

NC

I-H

110

5

NC

I-H

214

1

NC

I-H

109

2

NC

I-H

187

6

Lu-1

65

N

CI-

H5

10A

Lu

-14

3

NC

I-H

196

3

NC

I-H

208

1

NC

I-H

196

N

CI-

H1

694

D

MS7

9

RER

F-LC

-FM

M

S-1

-L

D

MS5

3

SHP

77

PIK3CA

1

10

100

1000

10000

Eto

po

sid

e (

nM

)

1

10

100

1000

Ge

dat

olis

ib (

nM

)




B

Figure 2

A

Geda

GAPDH

AKT

p-4EBP1

4EBP1

T308

S473

H1694 H69

- +

H187

- +

H446

- +

H1048

- +

DMS114

- +

H1092

- + - +

DMS53

- +

SHP77

- +

0.001

0.01

0.1

1

10

Ph

osp

ho

ryla

tio

n

(Ge

da/

DM

SO)

RP

S6

4EB

P1

4

EBP

2

RP

TOR

P

I4K

2A

A

S16

0

GR

B1

0

BC

LAF1

JU

N

ULK

1

FOX

K2

P

FKFB

2

AC

LY

CA

D

MY

CB

P2

LI

MA

1

TRIM

33

FL

NC

M

LLT4

A

CIN

1

SRR

M2

ST

MN

1

VA

PB

R

PLP

2

PU

M1

ZC

3H

13

C

HA

MP

1

TP5

3B

P1

N

MD

3

FOX

K1

H

NR

NP

U

FOX

O3

A

TF7

SM

AR

CA

4

SMA

RC

AD

1

UB

XN

7

PI3K pathway Others Increased Enzymes TF

Sensitive Cell Lines Resistant Cell Lines

Increase

Decrease




Figure 3

0

700

1400

H1

04

8D

MS1

14

H1

87

H4

46

Lu-1

39

H5

26

H1

69

4H

18

76

MS-

1-L

DM

S53

SHP

77

pm

ol/

pro

tein

(m

g)

total PIP2

0

125

250

H1

04

8

DM

S11

4

H1

87

H4

46

Lu-1

39

H5

26

H1

69

4

H1

87

6

MS-

1-L

DM

S53

SHP

77

pm

ol/

pro

tein

(m

g)

total PIP3

0

1

2

H1048DMSO

H1048Geda

pm

ol/

pro

tein

(m

g)

PIP3 (38:4)

0

60

120

H1048DMSO

H1048Geda

pm

ol/

pro

tein

(m

g)

PIP2 (38:4)

0

35

70

H1048DMSO

H1048Geda

pm

ol/

pro

tein

(m

g)

PIP1 (38:4)

0

15

30

H1

04

8

DM

S11

4

H1

87

H4

46

Lu-1

39

H5

26

H1

69

4

H1

87

6

MS-

1-L

DM

S53

SHP

77

pm

ol/

pro

tein

(m

g)

PIP3 (38:4)

0

125

250

H1

04

8

DM

S11

4

H1

87

H4

46

Lu-1

39

H5

26

H1

69

4

H1

87

6

MS-

1-L

DM

S53

SHP

77

pm

ol/

pro

tein

(m

g)

PIP2 (38:4)

A

B

C

p = 0.007

Sensitive Resistant Sensitive Resistant

Sensitive Resistant Sensitive Resistant

* * * * * * *




A B

C

Figure 4

0

2000

4000

0 10 20 30

Tum

or

size

(m

m3)

Time (days)

0

1500

3000

0 10 20 30

Tum

or

size

(m

m3)

Time (days)

H1048

SBC-5

+Vehicle +Gedatolisib

*

**

**

**

** **




0

50

100

100 10

Ce

ll Su

rviv

al (

%)

H1048

-8

-6

-4

-2

0

2

4

6L

og

2(S

en

sit

ive

/Res

ista

nt)

Figure 5

A

C

0

50

100

100 10

Ce

ll Su

rviv

al (

%)

DMS114

***

***

***

** +DMSO

+MTA

+DMSO

+MTA

100 nM 10 nM Gedatolisib

100 nM 10 nM Gedatolisib

50

Amino acids Purine

Central carbon

HXT*

Gln

Ad-SA R1P

AMP*

GMP* SA*

2-OG

UA*

XMP

High

Low

10 40

Orn*

30

IMP

B Purine Metabolism

Glycolysis PPP

Choline Metabolism

Polyamines and Creatine

Lipid Metabolism

Urea Cycle

TCA Cycle

Others

GUA

3-PG

NADP+




Figure 6

PI3K Pathway

AMP, IMP

Purine Salvage

PRPP HPRT1 APRT

Nucleotide Biosynthesis DNA & RNA

Glycolysis & PPP Glucose, G6P Ribose, PRPP

de novo Pathway

MTAP Adenine

Hypoxanthine

Gedatolisib

A

B p = 0.00311

C

0

70

140

siNC siHPRT1#1 siHPRT1#3

Cel

l Su

rviv

al (

%)

10 nM Gedatolisib

* *

0

15

30

siNC siHPRT1#1 siHPRT1#3

Ce

ll Su

rviv

al (

%)

100 nM Gedatolisib

**

0

0.1

0.2

H1

04

8

DM

S11

4

H1

87

H4

46

DM

S53

H1

69

4

Lu-1

43

DM

S79

H6

9

H2

02

9

H2

14

1

SBC

-5

SHP

77

H1

93

0

H5

10

A

H1

96

3

H1

09

2

H5

24

DM

S27

3

mR

NA

(H

PR

T1/G

AP

DH

)




-4

-2

0

2

4

6

0 10 20 30 40 50

Log 2

(SC

LC/N

on

-tu

mo

r)

AMP

SAM

ADP-rib

SAH Pro

3-PG

PEP

Thr

High

Low

Amino acids Purine

Central carbon

Gly Val

Ile

Ade

HXT

GMP

Put

S-AMP

Ala

Gua

S7P

DHAP

Figure 7 Research.

on January 7, 2021. © 2018 American Association for Cancercancerres.aacrjournals.org Downloaded from



Published OnlineFirst February 28, 2018.Cancer Res Hideki Makinoshima, Shigeki Umemura, Ayako Suzuki, et al. 3-kinase pathway inhibitor in small-cell lung carcinomaMetabolic determinants of sensitivity to phosphatidylinositol

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