Genome-wide identification of genetic determinants for the cytotoxicity of perifosine.

Perifosine belongs to the class of alkylphospholipid analogues, which act primarily at the cell membrane, thereby targeting signal transduction pathways. In phase I/II clinical trials, perifosine has induced tumour regression and caused disease stabilisation in a variety of tumour types. The genetic determinants responsible for its cytotoxicity have not been comprehensively studied, however. We performed a genome-wide analysis to identify genes whose expression levels or genotypic variation were correlated with the cytotoxicity of perifosine, using public databases on the US National Cancer Institute (NCI)-60 human cancer cell lines. For demonstrating drug specificity, the NCI Standard Agent Database (including 171 drugs acting through a variety of mechanisms) was used as a control. We identified agents with similar cytotoxicity profiles to that of perifosine in compounds used in the NCI drug screen. Furthermore, Gene Ontology and pathway analyses were carried out on genes more likely to be perifosine specific. The results suggested that genes correlated with perifosine cytotoxicity are connected by certain known pathways that lead to the mitogen-activated protein kinase signalling pathway and apoptosis. Biological processes such as 'response to stress', 'inflammatory response' and 'ubiquitin cycle' were enriched among these genes. Three single nucleotide polymorphisms (SNPs) located in CACNA2D1 and EXOC4 were found to be correlated with perifosine cytotoxicity. Our results provided a manageable list of genes whose expression levels or genotypic variation were strongly correlated with the cytotoxcity of perifosine. These genes could be targets for further studies using candidate-gene approaches. The results also provided insights into the pharmacodynamics of perifosine.

Introduction

Perifosine (NSC639966; Figure 1) belongs to the class of phospholipid analogues or alkylphospholipids, which have anticancer activity in both in vitro (cell culture studies) and in vivo (animal model-based studies) model systems [1,2]. Functionally, perifosine resembles natural phospholipids and acts primarily at the cell membrane, thereby targeting signal transduction pathways. Perifosine has been shown to inhibit, or otherwise modify, signal trans-duction through a number of different pathways, including mitogen-activated protein kinase (MAPK) and Akt [2-5]. Preclinical studies suggest that perifosine inhibits protein kinase B/Akt phosphorylation and induces in vitro and in vivo cytotoxicity in cancer cell lines such as multiple myeloma cells [4], HeLa cells [3] and prostate carcinoma cells [2]. Clinical studies have focused on daily oral dosing (after a loading dose), with two partial responses noted in soft tissue sarcoma (STS) patients, including one patient each with chondrosarcoma and leiomyosarcoma [6,7], as well as patients with renal cell carcinomas [7]. Furthermore, the phase II studies in STS patients were not designed to look for disease stabilisation, a potentially important endpoint for drugs targeting signal transduction pathways [8].

Figure 1

The chemical structure of perifosine (NSC639966). Molecular formula: C25H52NO4P; Chemical name: piperidinium, 4-[[hydroxy(octadecyloxy)phosphinyl]oxy]-1, 1-dimethyl-, inner salt.

The genetic determinants that are responsible for perifosine's activity have not been comprehensively studied, however. Traditional candidate-gene approaches require a priori knowledge and the selection of a small number of candidate genes for hypothesis testing, while an in silico genome-wide approach could be used to identify any associated genes as potential candidates in an unsupervised way. The US National Cancer Institute (NCI)-60 resources have allowed genome-wide studies using a panel of 60 human cancer cell lines [9]. In addition to the genetic determinants, the NCI-60 resources also provide tools such as COMPARE [10] to identify compounds that show correlated cytotoxic patterns with a particular agent. These compounds, for example, could be potential agents for enhancing the response to a candidate drug or as a substitute for that drug.

The NCI-60 human cancer cell lines have been used in anti-cancer drug screens conducted by the NCI since the late 1980s [9]. The cell lines represent nine distinct tumour types: leukaemia, colon, lung, brain, renal, melanoma, ovarian, breast and prostate. The Developmental Therapeutics Program (DTP) at NCI [11] has maintained a database for the cytotoxicity data, as represented by the GI50 (the concentration required to inhibit cell growth by 50 per cent) on > 40,000 cytotoxic agents, including perifosine [12]. A handful of gene expression datasets using high-throughput platforms such as the Affymetrix oligonucleotide microarrays and cDNA arrays of the untreated NCI-60 cell lines are now publicly available at the DTP/NCI website (Table 1). Recently, the NCI-60 cell lines were genotyped for ~120,000 single nucleotide polymorphism (SNP) markers using the Affymetrix Human 125K Mapping Array manufacturers details [13]. By associating gene expression or SNP genotypes in untreated NCI-60 cell lines, investigators have been able to predict the chemosensitivity of various cytotoxic compounds [14-16]. Here, we report a list of candidate genes whose expression levels or genotypic variation were found to be strongly correlated with the cytotoxicity of perifosine using these publicly available NCI-60 databases. The genes identified could be studied further using a candidate-gene approach. They also could provide new insights into the pharmacodynamics of perifosine.

Table 1

NCI-60 microarray expression datasets

Dataset	Institution	Platform	No. of genes on chip	No. of genes analyseda
MP-6800	Millenium Pharmaceuticals, Inc.	Affymetrix Human 6800	7,451	2,955
GL-U95	Gene Logic, Inc.	Affymetrix U95	63,175	23,223
NP-U95	Novartis Pharmaceuticals, Inc.	Affymetrix U95A	12,626	10,063
NS-cDNA [41,42]	NCI and Stanford University	cDNA array	9,703	5,291

aGenes or probe sets that had missing data in more than six cell lines (10 per cent) were not included in the analysis datasets.

NCI; National Cancer Institute.

Materials and methods

Cytotoxicity data

The 60 NCI-60 human cancer cell lines were originally exposed to > 40,000 compounds at NCI/ NIH and outside laboratories. The growth inhibitory effects of each compound were measured for each cell line and reported as the GI50 (for details, see the DTP/NCI website [17]) and maintained in the DTP/NCI online databases. Cytotoxicity data on perifosine (NSC639966) and other agents were obtained as the normalised -log10[GI50] values (released in September 2005). The NSC numbers and common names for the standard agents were retrieved from the DTP/NCI website.

COMPARE analysis

The COMPARE software [10,18,19] maintained at the DTP/NCI, was used to screen >40,000 synthetic or natural compounds for agents that showed correlated cell growth (GI50) patterns with that of perifosine. COMPARE generates rank-ordered lists of compounds based on the similarities of cytotoxicity patterns. Every compound from one of several specially prepared databases is ranked for similarity of its in vitro cell growth pattern to the in vitro cell growth pattern of a selected seed or probe compound (ie perifosine). Top-ranking agents based on Pearson correlation coefficient r, whose GI50 patterns correlated with that of perifosine, were reported by the software. To control false correlations due to small sample size, the minimum number of cell lines in common for two compounds to be included in the calculation was 50. We further set the cut-off for COMPARE analysis at |r| = 0.6 (equivalent to nominal p < 0.000001, assuming 40,000 compounds and n = 60, Bonferroni corrected p < 0.05).

NCI-60 microarray expression datasets

The NCI-60 microarray expression datasets (released in August 2005) were downloaded from the DTP/ NCI Molecular Target Databases [20]. These datasets comprise gene expression data on untreated NCI-60 cell lines using different microarray platforms (Table 1). Genes or probe sets that had missing data in more than six cell lines (10 per cent) were not included in the final analysis dataset.

NCI-60 SNP genotyes

The genotype calls for 125,937 SNPs in 58 NCI-60 cell lines were to be downloaded from the DTP/NCI website using the Affymetrix Human 125K Mapping array [13]. We removed uninformative SNPs, such as those with identical genotypes across all cell lines or those with missing data in more than six cell lines (10 per cent). Only SNP markers with at least two data points per genotype were included in the association studies. This left 34,040 highly informative SNPs in the final analysis dataset. Three exploratory genetic models (additive, dominant, recessive) were used to evaluate the association between genotype and cytotoxicity. Given the genotypes of a SNP marker (AA, AB, BB), the genotypes were encoded as (AA = 0, AB = 1, BB = 2) in the additive model, (AA/AB = 1, BB = 0) in the dominant model and (AA/AB = 0, BB = 1) in the recessive model.

Identifying associated copy number alterations

Data on copy number alterations in the NCI-60 cell lines as reported by Garraway and colleagues [13] were downloaded from the DTP/NCI website.

Linear regression model

We performed genome-wide associations between the gene expression (or genotype) and cytotoxicity data. Pearson correlation coefficients and the associated p-values were computed using a linear regression model, which was implemented as the lm function in the R Statistical Package [21]. Specifically, the cytotoxicity, as represented by -log10[GI50], was modelled as dependent on either gene expression or genotype. To adjust for multiple tests, the false discovery rate (FDR) was controlled using the Benjamini and Hochberg step-up FDR procedure [22] (FDRBH). An FDR cut-off of 10 per cent was used to identify candidates for further analyses.

Associations with standard agents

Associations between the identified genes and the cytotoxicity data on the 171 anti-cancer agents in the NCI Standard Agent Database [23] were performed to evaluate perifosine specificity for our gene list. The standard agents cover a variety of mechanisms, besides being phospholipid analogues, and were originally determined by Boyd [24]. The same cut-off (FDRBH < 0.10) was used to determine if an identified gene was associated significantly with any standard agents. The genes that showed no significant associations with any of the 171 standard agents using any dataset were denoted 'perifosine specific'. Genes that showed associations with any of the 171 standard agents using any dataset were denoted 'non-specific'.

Gene ontology and pathway analyses

We used Onto-Express and Pathway-Express [25-27] to search enriched biological processes and known physiological pathways among the perifosine-specific genes from the Gene Ontology (GO)[28] and Kyoto Encyclopaedia of Genes and Genomes (KEGG) databases [29,30]. GO terms or KEGG pathways that were over-represented relative to the corresponding analysis sets (two hits or more, binomial test at FDRBH < 0.05) were called 'enriched' in our gene list.

STS expression database

The identified perifosine-specific genes were queried against a STS expression database, which characterised eight gastrointestinal stromal tumours, eight monophasic synovial sarcomas, four liposarcomas, one myxoid, 11 leiomyosarcomas, eight malignant fibrous histiocytomas and two benign peripheral nerve sheath tumours (Schwannoma) [31]. Genes differentially expressed among different sarcomas were provided by the database using significance analysis of microarrays (SAM) [32].

Results

At p < 0.05 after Bonferroni correction, the COMPARE software [10,18] identified 24 agents with positive correlation with the cytotoxicity pattern of perifosine. By contrast, no agents with significant negative correlation were identified. Table 2 shows some top-ranking agents (r > 0.6) with well-characterised chemical names. Among them, some clearly belong to the same drug class as perifosine: miltefosine (NSC605583, r = 0.81) and edelfosine (NSC324368, r = 0.68). Edelfosine was further used as a representative of phospholipid analogues to verify the associations detected from perifosine (Supplementary Table 1 (Table 5)).

Table 2

Agents correlated with the GI50 values of perifosine, as reported by COMPARE (r > 0.6)

NSC#	r	Chemical name
605583	0.81	Miltefosin C; choline, hexadecyl hydrogen phosphate, inner salt
643826	0.75	Choline, hydroxide, 3-methoxy-2-[methyl(octadecyl)amino] propyl hydrogen phosphate, inner salt
643828	0.68	Choline, hydroxide, 2-methoxy-3-[methyl(octadecyl)amino] propyl hydrogen phosphate, inner salt
324368	0.68	Edelfosine; 1-octadecyl-2-methylphosphorylcholine
643827	0.68	Choline, hydroxide, 3-methoxy-1-[methyl(octadecyl)amino]-2-propyl hydrogen phosphate, inner salt
18268	0.65	Actinomycin D
678144	0.62	4H-l,3,6,2-dioxazaphosphocinium, 4-hexadecyltetrahydro- 2,6,6-trimethyl-, bromide, 2-oxide
337591	0.62	ES 12H; choline, hydroxide, 3-(dodecyloxy)propyl hydrogen phosphate, inner salt
87222	0.62	Actinomycin C3
266763	0.61	2-Propenamide, N-[2-(decylsulfinyl)-1-(hydroxymethyl)ethyl]-3-(1,2,3,4-tetrahydro-6-methyl-2,4-dioxo-5-pyrimidinyl)-
207895	0.60	Benzofurazan, 4-(4-methyl-l-piperazinyl)-7-nitro-, 3-oxide

Supplementary Table 1

Supplementary Table 1a. A majority of perifosine-specific gene expression levels are associated with the cytotoxicity of edelfosine

Symbol	Perifosine (p-value)	Edelfosine (p-value)	Perifosine (r-value)	Edelfosine (r-value)	Notes
NS-cDNA
ATF2	0.0000	0.0075	-0.560	-0.348	Significant
TRA2A	0.0008	0.0005	-0.438	-0.456	Significant
ETS2	0.0005	0.0004	-0.438	-0.449	Significant
UBE2D3	0.0009	0.0026	-0.419	-0.382	Significant
VEGFB	0.0006	0.0001	0.443	0.502	Significant
ANP32A	0.0002	0.0003	0.492	0.469	Significant
GL-U95
REG4	0.0000	0.0004	-0.579	-0.443	Significant
SLCO4A1	0.0000	0.0060	-0.518	-0.351	Significant
RPL18A	0.0000	0.0093	-0.500	-0.333	Significant
OAZ2	0.0000	0.0018	0.527	0.394	Significant
DZIP3	0.0000	0.0001	0.580	0.478	Significant
NP-U95
STK39	0.0001	0.0004	-0.479	-0.449	Significant
FAM32A	0.0002	0.0529	-0.460	-0.253	Marginal
MAPKAPK3	0.0003	0.0920	-0.454	-0.221	Marginal
RAB8A	0.0005	0.0018	-0.441	-0.399	Significant
STK17B	0.0006	0.0243	-0.435	-0.293	Significant
TCF3	0.0006	0.0054	-0.435	-0.358	Significant
PARP4	0.0006	0.0000	-0.433	-0.519	Significant
PSMA2	0.0006	0.0060	-0.432	-0.354	Significant
DGKE	0.0007	0.0019	-0.429	-0.396	Significant
PVT1	0.0010	0.0018	0.418	0.398	Significant
ELOVL2	0.0009	0.0005	0.419	0.438	Significant
SMARCA3	0.0009	0.0038	0.420	0.371	Significant
USP6	0.0007	0.0094	0.428	0.335	Significant
NFATC4	0.0005	0.0305	0.437	0.282	Significant
IGF1R	0.0005	0.0570	0.437	0.249	Marginal
POU4F1	0.0005	0.0023	0.439	0.390	Significant
PDLIM3	0.0005	0.0058	0.440	0.355	Significant
CBS	0.0004	0.0094	0.443	0.336	Significant
ARMCX2	0.0004	0.0008	0.447	0.425	Significant
OPHN1	0.0003	0.0194	0.459	0.304	Significant
ZNF609	0.0002	0.0091	0.462	0.337	Significant
ATN1	0.0001	0.0754	0.474	0.233	Marginal
DZIP3	0.0001	0.0093	0.479	0.336	Significant
PPBPL2	0.0001	0.0081	0.487	0.342	Significant
MPDZ	0.0000	0.0001	0.534	0.498	Significant
SKIV2L	0.0000	0.0007	0.557	0.427	Significant
GABRG3	0.0000	0.0000	0.601	0.533	Significant
Supplementary Table 1b. The perifosine-specific SNPs are associated with the cytotoxicity of edelfosine
dbSNP	Perifosine (p-value)	Perifosine (r-value)	Edelfosine (p-value)	Edelfosine (r-value)	Notes
rs4236669	2.80E-07	0.64	I.75E-03	0.42	Significant
rsI468400	8.80E-07	0.62	6.27E-04	0.46	Significant
rsI345938	2.60E-06	0.58	3.66E-05	0.52	Significant

Genes with expression associated with perifosine cytotoxicity and GO and pathway analyses

Table 3a lists the perifosine-specific genes identified from the microarray expression datasets. The non-specific genes are listed in Supplementary Table 2a (Table 6). The GO and pathway analyses were then carried out to find any enriched biological processes and known KEGG pathways among the perifosine-specific genes (Table 4).

Supplementary Table 2

Genes whose expression levels were associated with the cytotoxicity of perifosine (FDRBH < 0.10) but were not perifosine specific

Symbol	Gene title	r	p	Control totala
GL-U95
FNBP3	Formin-binding protein 3	-0.53	3.5E-05	75
MOBKL2A	MOBI, Mps One Binder kinase activator-like 2A (yeast)	-0.50	5.5E-05	43
TP53INP2	Tumour protein p53 inducible nuclear protein 2	0.51	3.9E-05	24
FBXO44	F-box protein 44	0.52	2.4E-05	53
TMF1	TATA element modulatory factor 1	0.53	1.7E-05	57
NP-U95
HNRPDL	Heterogeneous nuclear ribonucleoprotein D-like	-0.49	6.9E-05	3
DDX39	DEAD (Asp-Glu-Ala-Asp) box polypeptide 39	-0.49	7.3E-05	22
MRPL23	Mitochondrial ribosomal protein L23	-0.49	8.7E-05	2
RPS24	Ribosomal protein S24	-0.47	1.9E-04	21
LBR	Lamin B receptor	-0.47	2.0E-04	73
ERCC5	Excision repair cross-complementing rodent repair deficiency, complementation group 5	-0.47	2.0E-04	11
HDAC1	Histone deacetylase 1	-0.46	2.2E-04	2
GTF3A	General transcription factor IIIA	-0.45	3.IE-04	11
EEF1B2	Eukaryotic translation elongation factor 1 beta 2	-0.44	4.3E-04	68
ICAM3	Intercellular adhesion molecule 3	-0.44	5.IE-04	43
SNRPF	Small nuclear ribonucleoprotein polypeptide F	-0.44	5.2E-04	83
SH2D1A	SH2 domain protein IA, Duncan's disease (lymphoproliferative syndrome)	-0.44	5.8E-04	61
KIR3DL1	Killer cell immunoglobulin-like receptor, three domains, long cytoplasmic tail, 1	-0.43	7.3E-04	76
RPL35	Ribosomal protein L35	-0.42	8.IE-04	47
NUPL2	Nucleoporin-like 2	-0.42	8.8E-04	1
PTMA	Prothymosin, alpha (gene sequence 28)	-0.42	9.0E-04	33
CORO1A	Coronin, actin-binding protein, IA	-0.42	9.3E-04	104
LCNI	Lipocalin 1 (tear prealbumin)	-0.42	9.3E-04	21
POLE3	Polymerase (DNA directed), epsilon 3 (pl7 subunit)	-0.42	9.2E-04	31
RPS27A	Ribosomal protein S27a	-0.42	9.4E-04	79
TRIMI4	Tripartite motif-containing 14	-0.42	9.3E-04	60
LHFPL2	Lipoma HMGIC fusion partner-like 2	0.42	l.0E-03	99
DOK5	Docking protein 5	0.42	9.5E-04	l
EIF4GI	Eukaryotic translation initiation factor 4 gamma, l	0.42	9.6E-04	28
RGSI9	Regulator of G-protein signalling 19 interacting protein l	0.42	9.2E-04	116
COPB2	Coatomer protein complex, subunit beta 2 (beta prime)	0.42	8.9E-04	l
TLE2	Transducin-like enhancer of split 2 (E(spl) homologue, Drosophila)	0.42	9.0E-04	4
ITGA7	Integrin, alpha 7	0.42	8.6E-04	13
SEMA3C	Sema domain, immunoglobulin domain (Ig), short basic domain, secreted, (semaphorin) 3C	0.42	8.2E-04	22
SI00AI3	S100 calcium binding protein A13	0.43	7.8E-04	88
FLOTI	Flotillin 1	0.43	6.5E-04	2
MLFI	Myeloid leukemia factor 1	0.43	6.0E-04	11
ARHGEFII	Rho guanine nucleotide exchange factor (GEF) 11	0.44	5.5E-04	9
COLI5AI	Co11agen, type XV, alpha l	0.44	4.9E-04	l
DAGI	Dystroglycan l (dystrophin-associated glycoprotein l)	0.44	4.9E-04	118
IFNAI4	Interferon alpha l4	0.44	4.8E-04	2
PHLDBI	Pleckstrin homology-like domain, family B, member l	0.44	4.5E-04	12
PTPRS	Protein tyrosine phosphatase, receptor type S	0.45	4.0E-04	2
SASHI	SAM and SH3 domain containing l	0.45	4.lE-04	52
ACVRIB	Activin A receptor, type IB	0.45	3.2E-04	18
CTNNAI	Catenin (cadherin-associated protein), alpha 1, 102 kDa	0.46	3.0E-04	30
IL6ST	Interleukin 6 signal transducer (gp130, oncostatin M receptor)	0.46	2.9E-04	68
ATP6VID	ATPase, H+ transporting, lysosomal 34kDa, V1 subunit D	0.46	2.5E-04	2
SUOX	Sulphite oxidase	0.46	2.1E-04	58
TFAP2A	Transcription factor AP-2 alpha (activating enhancer binding protein 2 alpha)	0.48	1.3E-04	51
GRINA	Glutamate receptor, ionotropic, N-methyl D-asparate-associated protein 1 (glutamate binding)	0.49	9.3E-05	1
ABCB6	ATP-binding cassette, sub-family B (MDR/ TAP), member 6	0.49	8.2E-05	8
CTSF	Cathepsin F	0.49	7.4E-05	9
VEGFB	Vascular endothelial growth factor B	0.50	4.6E-05	6
GGCX	Gamma-glutamyl carboxylase	0.52	2.1E-05	23
LAPTM4B	Lysosomal-associated protein transmembrane 4 beta	0.53	1.6E-05	84
FYN	Sialidase 1 (lysosomal sialidase)	0.55	6.3E-06	59
NS-cDNA
LBR	Lamin B receptor	-0.47	1.3E-04	1
TRA2A	Transformer-2 alpha	-0.44	8.3E-04	13
ETS2	V-ets erythroblastosis virus E26 oncogene homolog 2 (avian)	-0.44	5.3E-04	30
UBE2D3	Ubiquitin-conjugating enzyme E2D 3 (UBC4/5 homologue, yeast)	-0.42	8.5E-04	4
RRMI	Ribonucleotide reductase M1 polypeptide	-0.41	9.8E-04	1
ATP6VICI	ATPase, H+ transporting, lysosomal 42kDa, V1 subunit C, isoform 1	0.42	7.7E-04	51
VEGFB	Vascular endothelial growth factor B	0.44	6.3E-04	57
RDX	Radixin	0.45	4.2E-04	29
APOD	Apolipoprotein D	0.45	3.1E-04	3
PTMS	Parathymosin	0.47	1.8E-04	2

aThe total number of associated standard agents (see Methods).

Table 4

Enriched Gene Ontology biological processes among the perifosine-specific genes

GO ID	Process	p	Gene symbol
NP-U95
GO:0006950	Response to stress	3.8E-04	STK39 MAPKAPK3
GO:0006511	Ubiquitin-dependent protein catabolism	7.9E-04	PSMA2 USP6
GO:0006954	Inflammatory response	4.3E-03	NFATC4 PARP4
GO:0006512	Ubiquitin cycle	6.5E-03	DZIP3 USP6
GO:0006366	Transcription from RNA polymerase II promoter	7.8E-03	PPBPL2 NFATC4
NS-cDNA
GO:0006355	Regulation of transcription, DNA-dependent	5.6E-03	EST2 ATF2

At FDRBH < 0.10, no genes were associated with perifosine cytotoxicty using the MP-6800 dataset, although at a more lenient cutoff (FDRBH < 0.25), one gene, FABP5 (encoding fatty acid binding protein 5), could be described as being significantly correlated with the sensitivity response to perifosine. The expression of FABP5 was denoted as non-specific, as it was also associated with one standard agent. For the two Affymetrix U95 series of microarray datasets (GL-U95 and NP-U95), one gene, DZIP3 (encoding zinc finger DAZ-interacting protein 3), was correlated with the resistance response to perifosine using both datasets (FDRBH < 0.10). DZIP3 was denoted as perifosine specific, as it showed no associations with any standard agents. In total, ten genes were found to be correlated with perifosine cytotoxicity (FDRBH < 0.10) using the GL-U95 dataset: five each with sensitivity and resistance. Of these, five did not show associations with any standard agents. The GO biological process 'ubiquitin cycle' was enriched among all ten genes (two hits or more, binomial test at FDRBH < 0.05); however, it was not significant among the five perifosine-specific genes. No KEGG pathways were enriched among the identified genes. By contrast, 79 genes were found to be correlated with perifosine cytotoxicity (FDRBH < 0.10) in the NP-U95 dataset. Among them, 30 genes were correlated with sensitivity, while 49 genes were correlated with resistance. Five GO biological processes were enriched among the 27 perifosine-specific genes (two hits or more, binomial test, FDRBH < 0.05). No KEGG pathways were enriched among the identified genes. Using the NS-cDNA dataset, 23 genes were identified, with significant associations with perifosine cytotoxicity (FDRBH < 0.10). Among them, 12 genes were correlated with sensitivity and 11 genes were correlated with resistance. One GO biological process, 'DNA-dependent regulation of transcription', was enriched among the five perifosine-specific genes. No KEGG pathways were enriched among the identified genes.

SNPs associated with perifosine cytotoxicity

Three SNPs under the recessive model were found to be significantly correlated with the resistance response to perifosine (FDRBH < 0.10; Table 3b, Figure 2). These included two SNPs located in the introns of CACNA2D1 (calcium channel, voltage-dependent, alpha 2/delta subunit 1). The third SNP is located in an intron of EXOC4 (exocyst complex component 4). Using both additive and dominant models, these three SNPs did not show significant associations with any standard agents. By contrast, rs1468400 in CACNA2D1 was correlated with one standard agent under the recessive model.

Table 3

Table 3a. Genes with Gene symbol expression levels specifically associated with the cytotoxicity of perifosine (FDRBH < 0.10)

Gene symbol	Gene title	ra	p	Response
GL-U95
REG4	Regenerating islet-derived family, member 4	-0.58	I.3E-06	Sensitivity
SLCO4A1	Solute carrier organic anion transporter family, member 4A1	-0.52	2.3E-05	Sensitivity
RPL18A	Ribosomal protein L18a	-0.50	4.7E-05	Sensitivity
OAZ2	Ornithine decarboxylase antizyme 2	0.53	I.6E-05	Resistance
DZIP3	Zinc finger DAZ-interacting protein 3	0.58	I.5E-06	Resistance
NP-U95
STK39	Serine threonine kinase 39 (STE20/ SPSI homologue, yeast)	- 0.48	I.2E-04	Sensitivity
FAM32A	Family with sequence similarity 32, member A	- 0.46	2.5E-04	Sensitivity
MAPKAPK3	Mitogen-activated protein kinase-activated protein kinase 3	- 0.45	3.0E-04	Sensitivity
RAB8A	RAB8A, member Ras oncogene family	- 0.44	4.7E-04	Sensitivity
STK17B	Serine/threonine kinase I7b (apoptosis-inducing)	- 0.44	5.8E-04	Sensitivity
TCF3	Transcription factor 3 (E2A immunoglobulin enhancer binding factors EI2/E47)	- 0.44	5.8E-04	Sensitivity
PARP4	Poly (ADP-ribose) polymerase family, member 4	- 0.43	6.IE-04	Sensitivity
PSMA2	Proteasome (prosome, macropain) subunit, alpha type, 2	- 0.43	6.3E-04	Sensitivity
DGKE	Diacylglycerol kinase, epsilon 64 kDa	- 0.43	6.9E-04	Sensitivity
PVT1	Pvtl oncogene homologue, MYC activator (mouse)	0.42	I.0E-03	Resistance
ELOVL2	Elongation of very long chain fatty acids (FENI/Elo2, SUR4/Elo3, yeast)-like 2	0.42	9.5E-04	Resistance
SMARCA3	SWI/SNF-related, matrix-associated, actin-dependent regulator of chromatin, subfamily a, member 3	0.42	9.3E-04	Resistance
USP6	TLI32 protein	0.43	7.IE-04	Resistance
IGF1R	Insulin-like growth factor I receptor	0.44	5.3E-04	Resistance
NFATC4	Nuclear factor of activated T-cells, cytoplasmic, calcineurin-dependent 4	0.44	5.4E-04	Resistance
POU4F1	POU domain, class 4, transcription factor I	0.44	5.IE-04	Resistance
PDLIM3	PDZ and LIM domain 3	0.44	5.0E-04	Resistance
CBS	Cystathionine beta-synthase	0.44	4.4E-04	Resistance
ARMCX2	Armadillo repeat containing, X-linked 2	0.45	3.9E-04	Resistance
OPHN1	Oligophrenin I	0.46	2.5E-04	Resistance
ZNF609	Zinc finger protein 609	0.46	2.3E-04	Resistance
ATN1	Atrophin I	0.47	I.5E-04	Resistance
DZIP3	Zinc finger DAZ-interacting protein 3	0.48	I.3E-04	Resistance
PPBPL2	Pro-platelet basic protein-like 2	0.49	9.3E-05	Resistance
MPDZ	Multiple PDZ domain protein	0.53	I.3E-05	Resistance
SKIV2L	Superkiller viralicidic activity 2-like (Saccharomyces cerevisiae)	0.56	4.6E-06	Resistance
GABRG3	Gamma-aminobutyric acid (GABA) A receptor, gamma 3	0.60	4.9E-07	Resistance
NS-cDNA
ATF2	Activating transcription factor 2	- 0.56	4.8E-06	Sensitivity
TRA2A	Transformer-2 alpha	- 0.44	8.3E-04	Sensitivity
ETS2	V-ets erythroblastosis virus E26 oncogene homologue 2 (avian)	- 0.44	5.3E-04	Sensitivity
UBE2D3	Ubiquitin-conjugating enzyme E2D 3 (UBC4/5 homologue, yeast)	- 0.42	8.5E-04	Sensitivity
ANP32A	Acidic (leucine-rich) nuclear phosphoprotein 32 family, member A	0.49	I.6E-04	Resistance
Table 3b. SNPs associated with the cytotoxicity of perifosine (FDRBH < 0.10)
dbSNPa	Gene locus	Location	r	p	Model
rs4236669	CACNA2DI	Intron	0.64	2.8E-07	Recessive
rsl468400	CACNA2DI	Intron	0.62	8.8E-07	Recessive
rs1345938	EXOC4	Intron	0.58	2.6E-06	Recessive

Table 3a. aPearson correlation coefficients were calculated by linear regression in which cytotoxicity (-log10[GI50]) was dependent on gene expression. A positive r-value indicates that a gene is correlated with resistance, while a negative r-value indicates that a gene is correlated with sensitivity.

Table 3b. adbSNP Build 126 (May, 2006).

Figure 2

SNPs specifically associated with the cytotoxicity of perifosine in the recessive model. AA/AB = 0; BB = 1. (A) Genotypes of rs4236669 in CACNA2DI were associated with the cytotoxicity of perifosine. (B) Genotypes of rs1345938 in EXOC4 were associated with the cytotoxicity of perifosine.

Copy number alterations and perifosine cytotoxicity

At FDRBH < 0.10, no copy number alterations or gene amplifications were found to be correlated with perifosine cytotoxicity.

Querying gene expression patterns in STS cells

Perifosine-specific genes in Table 3a were queried against the STS expression database [31]. Genes that are either up- or downregulated in each type of tumour are listed in Supplementary Table 3 (Table 7). Six genes (STK17B, IGF1R, POU4F1, CBS, MPDZ, EST2) were included in the database. With the exception of EST2, the other five genes were found to be up-or downregulated in certain STS cells.

Supplementary Table 3

Perifosine-specific genes whose expression levels are up- or downregulated in STS

Gene	ra	Calponin-positive leiomyosarcoma1b	Calponin-negative leiomyosarcoma1b	GIST1b	Synovial sarcoma1b	Liposarcomab	MFHb	Schwannoma1[3]
STKI7B	-0.44	Down regulated	Upregulated
IGFIR	0.44	Up regulated
P0U4FI	0.44	Down regulated	Down regulated		Upregulated			Down regulated
CBS	0.44	Up regulated				Down regulated
MPDZ	0.53	Up regulated	Down regulated
EST2	-0.44

aCorrelation coefficients (see Table 3 in the text).

bSTS type (see Nielsen et al. 2002).31

Discussion

We performed a genome-wide analysis to identify genes whose expression levels were significantly associated with perifosine's activity, as represented by its cytotoxicity (GI50). Four independent gene expression datasets of untreated NCI-60 cancer cell lines (Table 1), using different microarray platforms, were used to evaluate the association between cytotoxcity and gene expression. We further focused on the identified genes that are more likely to be perifosine specific (Table 3). Previous studies, using traditional candidate-gene approaches, have suggested that perifosine inhibits, or otherwise modifies, signal transduction through a number of different pathways, including MAPK and Akt [2-4]. An in silico genome-wide scan without a priori knowledge in this work provided more candidate genes in an unsupervised way.

The use of COMPARE [10,18] allowed us to identify compounds that have similar cell growth patterns with perifosine (Table 2). To limit the effects due to factors such as small sample size and multiple comparisons, we took measures to control potential false positives. Compounds including those belonging to the same drug class as perifosine (such as miltefosine and edelfosine) were among the top-ranking agents with strong positive correlation coefficients (r > 0.6, p < 0.05 after Bonferroni correction). Not surprisingly, a majority of the perifosine-specific genes were also significantly associated (nominal p < 0.05) with edelfosine, which was used to represent phospholipid analogues (Supplementary Table 1 (Table 5). The remaining few genes showed at least marginal associations (nominal p < 0.10) with edelfosine. This suggests that our list of perifosine-specific genes also contains a set of common genes that determines the pharmacodynamics of this drug class. To our knowledge, this is the most comprehensive list of associated genes for phospholipid analogues. The COMPARE program also retrieved drugs acting through different mechanisms (Table 2). The shared cytotoxicity profiles could be explained by the common pathways between these drugs and perifosine. For example, the correlation with actinomycin, which inhibits transcription by binding DNA at the transcription initiation complex and preventing elongation by RNA polymerase [33], could be explained via general transcriptional modulation (Table 4).

We wanted to know the interactions among the perifosine-specific genes with known biological processes or pathways. Searches against the GO and KEGG databases identified six biological processes that were enriched among the perifosine-specific genes (Table 4). Among them, the biological process of the ubiquitin cycle was identified with DZIP3 and USP6. Notably, DZIP3 was significantly associated with resistance to perifosine, using two of the Affymetrix U95 series of arrays (Table 3a). The function of DZIP3, a ubiquitin ligase [34], in the pharmacodynamics of perifosine has not been investigated, although, given the potential of ubiquitin ligases as anti-cancer targets [35,36], DZIP3 and the role of ubiquitin-dependent protein degradation could be an interesting candidate for further studies. The perifosine-specific genes also over-represented such biological processes as 'response to stress' and 'inflammatory response', which are more evidently related to drug response. Although no particular known KEGG pathways were found to be enriched among the perifosine-specific genes, many of these genes could be connected by a network of known physiological pathways (Figure 3) which have interactions with perifosine through known mechanisms that lead to the MAPK signalling pathway and apoptosis. For example, perifosine can affect the phosphatidylinositol signalling pathway, Akt signalling pathway and MAPK signalling pathway [37,38]. Some of our identified perifosine-specific genes are known to be involved in these pathways; for example, DGKE (the phosphatidylinositol signalling pathway) and MAPKAPK3 (the MAPK signalling pathway). Furthermore, the gene product of DGKE is involved in the phosphatidylinositol signalling system pathway and interacts with the phosphatidylinositol 3-kinase/phosphatase and tensin homologue deleted on chromosome 10 (PTEN)/ Akt pathways [30], suggesting its potential role in the perifosine response. The connected pathways can be divided into three categories:[29] cell communication (tight junction, adherens junction and focal adhesion); immune systems (T/B cell receptor signalling pathways); and signal transduction (MAPK, Wnt, vascular endothelial growth factor and phosphatidylinositol signalling pathways). Given the fact that perifosine, as well as edelfosine, significantly affects the pathway of extrinsic apoptosis [38-40], our findings showed that while perifosine was involved in such pathways as the MAPK and phosphatidylinositol signalling pathways that can lead to apoptosis [2-4], it could also influence other interconnected pathways, such as those in cell communication.

Figure 3

Some perifosine-specific genes are connected by common pathways leading to MAPK signalling pathway and apoptosis. Signal transduction pathways: ASP Akt signalling pathway; PSS, phosphatidylinositol signalling system; WSP: Wnt signalling pathway; VSP vascular endothelial growth factor signalling pathway; MSP, MAPK signalling pathway. Cell communication pathways: TJ, tight junction; AJ, adherens junction; FA, focal adhesion. Immune systems: TSP, T-cell receptor signalling pathway; BSP, B-cell receptor signalling pathway. The relationships among pathways were retrieved from the KEGG database (Release 45.0, January 1, 2008).

Variation in DNA sequence is partially responsible for gene expression; [41,42] therefore, we performed an association test between SNP [13] genotypes and the cytotoxicity of perifosine. Different models (additive, dominant and recessive) were used to explore the genetic relationships between genotypes and cytotoxicity. Two SNPs (rs4236669 in CACNA2D1 and rs1345938 in EXOC4) showed strong perifosine-specific associations under the recessive model (Figure 2). Since the expression of CACNA2D1 was not found to be significantly correlated with perifosine cytotoxicity, the relationship between gene expression and its genotypes is not straightforward. Given that CACNA2D1 is involved in the MAPK signalling pathway [29], however, these SNPs could be interesting candidates for further studies.

Studies have shown that alkylphospholipids are a class of anti-cancer agents that perturb signal transduction pathways through inhibition of MAPK [2-4] and Akt phosphorylation. These drugs have shown consistent clinical anti-cancer activity, but their systemic application has been limited by toxicity. Therefore, one impact of our list of genes could be to help to identify better targeted cancer types for perifosine. One potential candidate, for example, could be multiple myeloma, given the fact that the PSMA2 gene (associated with the sensitivity response to perifosine; Table 3a) was found to be highly upregulated in multiple myeloma cells [43]. In fact, perifosine activity has been reported in myeloma preclinically [4,39]. A recent multicentre phase II study of perifosine alone and in combination with dexamethasone for patients with relapsed or relapsed/refractory multiple myeloma suggested promising activity (eg stabilisation of disease) as combination therapy, with manageable toxicity [44]. Our results thus warrant further clinical trials for this tumour type. There is some evidence of perifosine having activity in STS, with responses reported in chondrosarcoma and leiomyosarcoma [6,7]. Based on these studies, continued assessment of perifosine in STS also appears to be warranted. Given the heterogeneity of STS, it is a plausible hypothesis that there is an identifiable subset of tumours that will respond to this agent [45]. A search against a STS expression database [31] further indicated that a type of leiomyosarcomas that does not express calponin showed the best correlated pattern of gene expression with our perifosine-specific genes (Supplementary Table 4). For example, STK17B (associated with the sensitivity response to perifosine; Table 3a) is significantly upregulated in this tumour type, while POU4F1 and MPDZ (associated with the resistance response to perifosine; Table 3a) are significantly downregulated in this tumour type, suggesting that this type of leiomyo-sarcoma could be a better target for perifosine. As the available STS expression dataset contains only ~5,000 genes [31], a more comprehensive dataset could provide more insights.

In summary, we used the public NCI-60 resources to identify a list of genes potentially relevant to the cytotoxicity of perifosine. Although there were some limitations; such as the gene coverage of the current microarray platforms, relatively small sample size of 60 cell lines and severity of multiple comparisons, our results not only confirmed that perifosine is involved in some known pathways (eg MAPK signalling) that can lead to apoptosis, but also suggested that it could influence some new candidate genes and pathways. Our unsupervised in silico analyses, therefore, could provide targeted candidates that are globally associated with the perifosine response for further studies.