A systems analysis of the chemosensitivity of breast cancer cells to the polyamine analogue PG-11047

The Synergistic Benefit of Combination Strategies Targeting Tumor Cell Polyamine Homeostasis

Mammalian polyamines, including putrescine, spermidine, and spermine, are positively charged amines that are essential for all living cells including neoplastic cells. An increasing understanding of polyamine metabolism, its molecular functions, and its role in cancer has led to the interest in targeting polyamine metabolism as an anticancer strategy, as the metabolism of polyamines is frequently dysregulated in neoplastic disease. In addition, due to compensatory mechanisms, combination therapies are clinically more promising, as agents can work synergistically to achieve an effect beyond that of each strategy as a single agent. In this article, the nature of polyamines, their association with carcinogenesis, and the potential use of targeting polyamine metabolism in treating and preventing cancer as well as combination therapies are described. The goal is to review the latest strategies for targeting polyamine metabolism, highlighting new avenues for exploiting aberrant polyamine homeostasis for anticancer therapy and the mechanisms behind them.

Spermine oxidase induces DNA damage and sensitizes fusion negative rhabdomyosarcoma cells to irradiation

Rhabdomyosarcoma (RMS) is a pediatric myogenic soft tissue sarcoma that includes fusion-positive (FP) and fusion-negative (FN) molecular subtypes. FP-RMS expresses PAX3-FOXO1 fusion protein and often shows dismal prognosis. FN-RMS shows cytogenetic abnormalities and frequently harbors RAS pathway mutations. Despite the multimodal heavy chemo and radiation therapeutic regimens, high risk metastatic/recurrent FN-RMS shows a 5-year survival less than 30% due to poor sensitivity to chemo-radiotherapy. Therefore, the identification of novel targets is needed. Polyamines (PAs) such as putrescine (PUT), spermidine (SPD) and spermine (SPM) are low-molecular-mass highly charged molecules whose intracellular levels are strictly modulated by specific enzymes. Among the latter, spermine oxidase (SMOX) regulates polyamine catabolism oxidizing SPM to SPD, which impacts cellular processes such as apoptosis and DNA damage response. Here we report that low SMOX levels are associated with a worse outcome in FN-RMS, but not in FP-RMS, patients. Consistently, SMOX expression is downregulated in FN-RMS cell lines as compared to normal myoblasts. Moreover, SMOX transcript levels are reduced FN-RMS cells differentiation, being indirectly downregulated by the muscle transcription factor MYOD. Noteworthy, forced expression of SMOX in two cell lines derived from high-risk FN-RMS: 1) reduces SPM and upregulates SPD levels; 2) induces G0/G1 cell cycle arrest followed by apoptosis; 3) impairs anchorage-independent and tumor spheroids growth; 4) inhibits cell migration; 5) increases γH2AX levels and foci formation indicative of DNA damage. In addition, forced expression of SMOX and irradiation synergize at activating ATM and DNA-PKCs, and at inducing γH2AX expression and foci formation, which suggests an enhancement in DNA damage response. Irradiated SMOX-overexpressing FN-RMS cells also show significant decrease in both colony formation capacity and spheroids growth with respect to single approaches. Thus, our results unveil a role for SMOX as inhibitor of tumorigenicity of FN-RMS cells in vitro. In conclusion, our in vitro results suggest that SMOX induction could be a potential combinatorial approach to sensitize FN-RMS to ionizing radiation and deserve further in-depth studies.

Hypoxia classifier for transcriptome datasets

Molecular gene signatures are useful tools to characterize the physiological state of cell populations, but most have developed under a narrow range of conditions and cell types and are often restricted to a set of gene identities. Focusing on the transcriptional response to hypoxia, we aimed to generate widely applicable classifiers sourced from the results of a meta-analysis of 69 differential expression datasets which included 425 individual RNA-seq experiments from 33 different human cell types exposed to different degrees of hypoxia (0.1-5%[Formula: see text]) for 2-48 h. The resulting decision trees include both gene identities and quantitative boundaries, allowing for easy classification of individual samples without control or normoxic reference. Each tree is composed of 3-5 genes mostly drawn from a small set of just 8 genes (EGLN1, MIR210HG, NDRG1, ANKRD37, TCAF2, PFKFB3, BHLHE40, and MAFF). In spite of their simplicity, these classifiers achieve over 95% accuracy in cross validation and over 80% accuracy when applied to additional challenging datasets. Our results indicate that the classifiers are able to identify hypoxic tumor samples from bulk RNAseq and hypoxic regions within tumor from spatially resolved transcriptomics datasets. Moreover, application of the classifiers to histological sections from normal tissues suggest the presence of a hypoxic gene expression pattern in the kidney cortex not observed in other normoxic organs. Finally, tree classifiers described herein outperform traditional hypoxic gene signatures when compared against a wide range of datasets. This work describes a set of hypoxic gene signatures, structured as simple decision tress, that identify hypoxic samples and regions with high accuracy and can be applied to a broad variety of gene expression datasets and formats.

Polyamine homeostasis-based strategies for cancer: The role of combination regimens

Spermine, spermidine and putrescine polyamines are naturally occurring ubiquitous positively charged amines and are essential metabolites for biological functions in our life. These compounds play a crucial role in many cell processes, including cellular proliferation, growth, and differentiation. Intracellular levels of polyamines depend on their biosynthesis, transport and degradation. Polyamine levels are high in cancer cells, which leads to the promotion of tumor growth, invasion and metastasis. Targeting polyamine metabolism as an anticancer strategy is considerably rational. Due to compensatory mechanisms, a single strategy does not achieve satisfactory clinical effects when using a single agent. Combination regimens are more clinically promising for cancer chemoprevention because they work synergistically with causing little or no adverse effects due to each individual agent being used at lower doses. Moreover, bioactive substances have advantages over single chemical agents because they can affect multiple targets. In this review, we discuss anticancer strategies targeting polyamine metabolism and describe how combination treatments and effective natural active ingredients are promising therapies. The existing research suggests that polyamine metabolic enzymes are important therapeutic targets and that combination therapies can be more effective than monotherapies based on polyamine depletion.

Metabolism and function of polyamines in cancer progression

Polyamines are essential for the proliferation, differentiation, and development of eukaryotes. They include spermine, spermidine, and the diamine precursor putrescine, and are low-molecular-weight, organic polycations with more than two amino groups. Their intracellular concentrations are strictly maintained within a specific physiological range through several regulatory mechanisms in normal cells. In contrast, polyamine metabolism is dysregulated in many neoplastic states, including cancer. In various types of cancer, polyamine levels are elevated, and crosstalk occurs between polyamine metabolism and oncogenic pathways, such as mTOR and RAS pathways. Thus, polyamines might have potential as therapeutic targets in the prevention and treatment of cancer. The molecular mechanisms linking polyamine metabolism to carcinogenesis must be unraveled to develop novel inhibitors of polyamine metabolism. This overview describes the nature of polyamines, their association with carcinogenesis, the development of polyamine inhibitors and their potential, and the findings of clinical trials.

A Phase Ib multicenter, dose-escalation study of the polyamine analogue PG-11047 in combination with gemcitabine, docetaxel, bevacizumab, erlotinib, cisplatin, 5-fluorouracil, or sunitinib in patients with advanced solid tumors or lymphoma

PURPOSE

Polyamines are absolutely essential for maintaining tumor cell proliferation. PG-11047, a polyamine analogue, is a nonfunctional competitor of the natural polyamine spermine that has demonstrated anticancer activity in cells and animal models of multiple cancer types. Preclinical investigations into the effects of common chemotherapeutic agents have revealed overlap with components of the polyamine metabolic pathway also affected by PG-11047. This report describes a Phase Ib clinical trial investigating PG-11047 in combination with cytotoxic and anti-angiogenic chemotherapeutic agents in patients with advanced refractory metastatic solid tumors or lymphoma.

METHODS

A total of 172 patients were assigned to treatment arms based on cancer type to receive the appropriate standard-of-care therapy (gemcitabine, docetaxel, bevacizumab, erlotinib, cisplatin, 5-fluorouracil (5-FU), or sunitinib as directed) along with once weekly intravenous infusions of PG-11047. PG-11047 dose escalation ranged from 50 to 590 mg.

RESULTS

The maximum tolerated dose (MTD) of PG-11047 in combination with bevacizumab, erlotinib, cisplatin, and 5-FU was 590 mg. Dose-limiting toxicities (DLTs) in these groups were rare (5 of 148 patients). Overall partial responses (PR) were observed in 12% of patients treated with PG-11047 and bevacizumab, with stable disease documented in an additional 40%. Stable disease occurred in 71.4% of patients in the 5-FU arm, 54.1% in the cisplatin arm, and 33.3% in the erlotinib arm. Four of the patients receiving cisplatin + PG-11047 (20%) had unconfirmed PRs. MTDs for gemcitabine, docetaxel, and sunitinib could not be determined due to DLTs at low doses of PG-11047 and small sample size.

CONCLUSIONS

Results of this Phase Ib trial indicate that PG-11047 can be safely administered to patients in combination with bevacizumab, erlotinib, cisplatin, and 5-FU on the once weekly dosing schedule described and may provide therapeutic benefit. The manageable toxicity profile and high MTD determination provide a safety profile for further clinical studies.

A phase I dose-escalation study of the polyamine analog PG-11047 in patients with advanced solid tumors

PURPOSE

Polyamines are essential for the sustained proliferation and biomass required by tumor cells. Bis-alkylated polyamine analogs are nonfunctional competitors of natural polyamines. Of these, PG-11047, a second-generation unsaturated analog of the polyamine spermine, has demonstrated anticancer activity in cell lines and animal models of multiple cancer types. This report describes the first phase I clinical trial to investigate PG-11047 in patients with advanced refractory metastatic solid tumors.

METHODS

Forty-six patients were treated with 60-min intravenous infusions of PG-11047 using a 28-day dosing cycle with treatments on days 1, 8, and 15. Doses ranged from 50 to 750 mg. The treatment period consisted of at least two cycles.

RESULTS

The maximum tolerated dose of PG-11047 administered at this dosing schedule was 610 mg. Dose-limiting toxicities (DLT) were mainly gastrointestinal, including oral/anal mucositis and diarrhea; other DLTs included one case each of angioedema and a grade 3 alanine aminotransferase (ALT) increase. The most common adverse effects were fatigue and anorexia. Stable disease was documented in 30% of patients.

CONCLUSION

Results of this phase I trial suggest that PG-11047 can be safely administered to patients on the once weekly dosing schedule described. The manageable toxicity profile and high MTD determination provide a safety profile for further clinical studies, including those in combination with current chemotherapeutic agents.

Microenvironment-Mediated Mechanisms of Resistance to HER2 Inhibitors Differ between HER2+ Breast Cancer Subtypes

Extrinsic signals are implicated in breast cancer resistance to HER2-targeted tyrosine kinase inhibitors (TKIs). To examine how microenvironmental signals influence resistance, we monitored TKI-treated breast cancer cell lines grown on microenvironment microarrays composed of printed extracellular matrix proteins supplemented with soluble proteins. We tested ~2,500 combinations of 56 soluble and 46 matrix microenvironmental proteins on basal-like HER2+ (HER2E) or luminal-like HER2+ (L-HER2+) cells treated with the TKIs lapatinib or neratinib. In HER2E cells, hepatocyte growth factor, a ligand for MET, induced resistance that could be reversed with crizotinib, an inhibitor of MET. In L-HER2+ cells, neuregulin1-β1 (NRG1β), a ligand for HER3, induced resistance that could be reversed with pertuzumab, an inhibitor of HER2-HER3 heterodimerization. The subtype-specific responses were also observed in 3D cultures and murine xenografts. These results, along with bioinformatic pathway analysis and siRNA knockdown experiments, suggest different mechanisms of resistance specific to each HER2+ subtype: MET signaling for HER2E and HER2-HER3 heterodimerization for L-HER2+ cells.

Quantification of sensitivity and resistance of breast cancer cell lines to anti-cancer drugs using GR metrics

Traditional means for scoring the effects of anti-cancer drugs on the growth and survival of cell lines is based on relative cell number in drug-treated and control samples and is seriously confounded by unequal division rates arising from natural biological variation and differences in culture conditions. This problem can be overcome by computing drug sensitivity on a per-division basis. The normalized growth rate inhibition (GR) approach yields per-division metrics for drug potency (GR₅₀) and efficacy (GR_max) that are analogous to the more familiar IC₅₀ and E_max values. In this work, we report GR-based, proliferation-corrected, drug sensitivity metrics for ~4,700 pairs of breast cancer cell lines and perturbagens. Such data are broadly useful in understanding the molecular basis of therapeutic response and resistance. Here, we use them to investigate the relationship between different measures of drug sensitivity and conclude that drug potency and efficacy exhibit high variation that is only weakly correlated. To facilitate further use of these data, computed GR curves and metrics can be browsed interactively at http://www.GRbrowser.org/.

Biochemical evaluation of the anticancer potential of the polyamine-based nanocarrier Nano11047

Synthesizing polycationic polymers directly from existing drugs overcomes the drug-loading limitations often associated with pharmacologically inert nanocarriers. We recently described nanocarriers formed from a first-generation polyamine analogue, bis(ethyl)norspermine (BENSpm), that could simultaneously target polyamine metabolism while delivering therapeutic nucleic acids. In the current study, we describe the synthesis and evaluation of self-immolative nanocarriers derived from the second-generation polyamine analogue PG-11047. Polyamines are absolutely essential for proliferation and their metabolism is frequently dysregulated in cancer. Through its effects on polyamine metabolism, PG-11047 effectively inhibits tumor growth in cancer cell lines of multiple origins as well as in human tumor mouse xenografts. Promising clinical trials have been completed verifying the safety and tolerance of this rotationally restricted polyamine analogue. We therefore used PG-11047 as the basis for Nano11047, a biodegradable, prodrug nanocarrier capable of targeting polyamine metabolism. Following exposure of lung cancer cell lines to Nano11047, uptake and intracellular degradation into the parent compound PG-11047 was observed. The release of PG-11047 highly induced the polyamine catabolic enzyme activities of spermidine/spermine N1-acetyltransferase (SSAT) and spermine oxidase (SMOX). By contrast, the activity of ornithine decarboxylase (ODC), a rate-limiting enzyme in polyamine biosynthesis and a putative oncogene, was decreased. Consequently, intracellular levels of the natural polyamines were depleted concurrent with tumor cell growth inhibition. This availability of Nano11047 as a novel drug form and potential nucleic acid delivery vector will potentially benefit and encourage future clinical studies.

Integrative exploration of genomic profiles for triple negative breast cancer identifies potential drug targets

BACKGROUND

Triple negative breast cancer (TNBC) is high-risk due to its rapid drug resistance and recurrence, metastasis, and lack of targeted therapy. So far, no molecularly targeted therapeutic agents have been clinically approved for TNBC. It is imperative that we discover new targets for TNBC therapy.

OBJECTIVES

A large volume of cancer genomics data are emerging and advancing breast cancer research. We may integrate different types of TNBC genomic data to discover molecular targets for TNBC therapy.

DATA SOURCES

We used publicly available TNBC tumor tissue genomic data in the Cancer Genome Atlas database in this study.

METHODS

We integratively explored genomic profiles (gene expression, copy number, methylation, microRNA [miRNA], and gene mutation) in TNBC and identified hyperactivated genes that have higher expression, more copy numbers, lower methylation level, or are targets of miRNAs with lower expression in TNBC than in normal samples. We ranked the hyperactivated genes into different levels based on all the genomic evidence and performed functional analyses of the sets of genes identified. More importantly, we proposed potential molecular targets for TNBC therapy based on the hyperactivated genes.

RESULTS

Some of the genes we identified such as FGFR2, MAPK13, TP53, SRC family, MUC family, and BCL2 family have been suggested to be potential targets for TNBC treatment. Others such as CSF1R, EPHB3, TRIB1, and LAD1 could be promising new targets for TNBC treatment. By utilizing this integrative analysis of genomic profiles for TNBC, we hypothesized that some of the targeted treatment strategies for TNBC currently in development are more likely to be promising, such as poly (ADP-ribose) polymerase inhibitors, while the others are more likely to be discouraging, such as angiogenesis inhibitors.

LIMITATIONS

The findings in this study need to be experimentally validated in the future.

CONCLUSION

This is a systematic study that combined 5 different types of genomic data to molecularly characterize TNBC and identify potential targets for TNBC therapy. The integrative analysis of genomic profiles for TNBC could assist in identifying potential new therapeutic targets and predicting the effectiveness of a targeted treatment strategy for TNBC therapy.

In vitro human cell line models to predict clinical response to anticancer drugs

In vitro human cell line models have been widely used for cancer pharmacogenomic studies to predict clinical response, to help generate pharmacogenomic hypothesis for further testing, and to help identify novel mechanisms associated with variation in drug response. Among cell line model systems, immortalized cell lines such as Epstein-Barr virus (EBV)-transformed lymphoblastoid cell lines (LCLs) have been used most often to test the effect of germline genetic variation on drug efficacy and toxicity. Another model, especially in cancer research, uses cancer cell lines such as the NCI-60 panel. These models have been used mainly to determine the effect of somatic alterations on response to anticancer therapy. Even though these cell line model systems are very useful for initial screening, results from integrated analyses of multiple omics data and drug response phenotypes using cell line model systems still need to be confirmed by functional validation and mechanistic studies, as well as validation studies using clinical samples. Future models might include the use of patient-specific inducible pluripotent stem cells and the incorporation of 3D culture which could further optimize in vitro cell line models to improve their predictive validity.

Decoupling of the PI3K Pathway via Mutation Necessitates Combinatorial Treatment in HER2+ Breast Cancer

We report here on experimental and theoretical efforts to determine how best to combine drugs that inhibit HER2 and AKT in HER2⁺ breast cancers. We accomplished this by measuring cellular and molecular responses to lapatinib and the AKT inhibitors (AKT_i) GSK690693 and GSK2141795 in a panel of 22 HER2⁺ breast cancer cell lines carrying wild type or mutant PIK3CA. We observed that combinations of lapatinib plus AKT_i were synergistic in HER2⁺/PIK3CA^mut cell lines but not in HER2⁺/PIK3CA^wt cell lines. We measured changes in phospho-protein levels in 15 cell lines after treatment with lapatinib, AKT_i or lapatinib + AKT_i to shed light on the underlying signaling dynamics. This revealed that p-S6RP levels were less well attenuated by lapatinib in HER2⁺/PIK3CA^mut cells compared to HER2⁺/PIK3CA^wt cells and that lapatinib + AKT_i reduced p-S6RP levels to those achieved in HER2⁺/PIK3CA^wt cells with lapatinib alone. We also found that that compensatory up-regulation of p-HER3 and p-HER2 is blunted in PIK3CA^mut cells following lapatinib + AKT_i treatment. Responses of HER2⁺ SKBR3 cells transfected with lentiviruses carrying control or PIK3CA^mut sequences were similar to those observed in HER2⁺/PIK3CA^mut cell lines but not in HER2⁺/PIK3CA^wt cell lines. We used a nonlinear ordinary differential equation model to support the idea that PIK3CA mutations act as downstream activators of AKT that blunt lapatinib inhibition of downstream AKT signaling and that the effects of PIK3CA mutations can be countered by combining lapatinib with an AKT_i. This combination does not confer substantial benefit beyond lapatinib in HER2⁺/PIK3CA^wt cells.

An ensemble based top performing approach for NCI-DREAM drug sensitivity prediction challenge

We consider the problem of predicting sensitivity of cancer cell lines to new drugs based on supervised learning on genomic profiles. The genetic and epigenetic characterization of a cell line provides observations on various aspects of regulation including DNA copy number variations, gene expression, DNA methylation and protein abundance. To extract relevant information from the various data types, we applied a random forest based approach to generate sensitivity predictions from each type of data and combined the predictions in a linear regression model to generate the final drug sensitivity prediction. Our approach when applied to the NCI-DREAM drug sensitivity prediction challenge was a top performer among 47 teams and produced high accuracy predictions. Our results show that the incorporation of multiple genomic characterizations lowered the mean and variance of the estimated bootstrap prediction error. We also applied our approach to the Cancer Cell Line Encyclopedia database for sensitivity prediction and the ability to extract the top targets of an anti-cancer drug. The results illustrate the effectiveness of our approach in predicting drug sensitivity from heterogeneous genomic datasets.

Quantitative scoring of differential drug sensitivity for individually optimized anticancer therapies

We developed a systematic algorithmic solution for quantitative drug sensitivity scoring (DSS), based on continuous modeling and integration of multiple dose-response relationships in high-throughput compound testing studies. Mathematical model estimation and continuous interpolation makes the scoring approach robust against sources of technical variability and widely applicable to various experimental settings, both in cancer cell line models and primary patient-derived cells. Here, we demonstrate its improved performance over other response parameters especially in a leukemia patient case study, where differential DSS between patient and control cells enabled identification of both cancer-selective drugs and drug-sensitive patient sub-groups, as well as dynamic monitoring of the response patterns and oncogenic driver signals during cancer progression and relapse in individual patient cells ex vivo. An open-source and easily extendable implementation of the DSS calculation is made freely available to support its tailored application to translating drug sensitivity testing results into clinically actionable treatment options.

A community effort to assess and improve drug sensitivity prediction algorithms

Predicting the best treatment strategy from genomic information is a core goal of precision medicine. Here we focus on predicting drug response based on a cohort of genomic, epigenomic and proteomic profiling data sets measured in human breast cancer cell lines. Through a collaborative effort between the National Cancer Institute (NCI) and the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we analyzed a total of 44 drug sensitivity prediction algorithms. The top-performing approaches modeled nonlinear relationships and incorporated biological pathway information. We found that gene expression microarrays consistently provided the best predictive power of the individual profiling data sets; however, performance was increased by including multiple, independent data sets. We discuss the innovations underlying the top-performing methodology, Bayesian multitask MKL, and we provide detailed descriptions of all methods. This study establishes benchmarks for drug sensitivity prediction and identifies approaches that can be leveraged for the development of new methods.

Histone deacetylase inhibition overcomes drug resistance through a miRNA-dependent mechanism

The treatment of specific tumor cell lines with poly- and oligoamine analogs results in a superinduction of polyamine catabolism that is associated with cytotoxicity; however, other tumor cells show resistance to analog treatment. Recent data indicate that some of these analogs also have direct epigenetic effects. We, therefore, sought to determine the effects of combining specific analogs with an epigenetic targeting agent in phenotypically resistant human lung cancer cell lines. We show that the histone deacetylase inhibitor MS-275, when combined with (N(1), N(11))-bisethylnorspermine (BENSpm) or (N(1), N(12))-bis(ethyl)-cis-6,7-dehydrospermine tetrahydrochloride (PG-11047), synergistically induces the polyamine catabolic enzyme spermidine/spermine N(1)-acetyltransferase (SSAT), a major determinant of sensitivity to the antitumor analogs. Evidence indicates that the mechanism of this synergy includes reactivation of miR-200a, which targets and destabilizes kelch-like ECH-associated protein 1 (KEAP1) mRNA, resulting in the translocation and binding of nuclear factor (erythroid-derived 2)-like 2 (NRF2) to the polyamine-responsive element of the SSAT promoter. This transcriptional stimulation, combined with positive regulation of SSAT mRNA and protein by the analogs, results in decreased intracellular concentrations of natural polyamines and growth inhibition. The finding that an epigenetic targeting agent is capable of inducing a rate-limiting step in polyamine catabolism to overcome resistance to the antitumor analogs represents a completely novel chemotherapeutic approach. In addition, this is the first demonstration of miRNA-mediated regulation of the polyamine catabolic pathway. Furthermore, the individual agents used in this study have been investigated clinically; therefore, translation of these combinations into the clinical setting holds promise.

Quantitative high-throughput efficacy profiling of approved oncology drugs in inflammatory breast cancer models of acquired drug resistance and re-sensitization

Although there is no standard treatment protocol for inflammatory breast cancer (IBC), multi-modality treatment has improved survival. In this study we profiled the NCI approved oncology drug set in a qHTS format to identify those that are efficacious in basal type and ErbB2 overexpressing IBC models. Further, we characterized the sensitivity of an acquired therapeutic resistance model to the oncology drugs. We observed that lapatinib-induced acquired resistance in SUM149 cells led to cross-resistance to other targeted- and chemotherapeutic drugs. Removal of the primary drug to which the model was developed led to re-sensitization to multiple drugs to a degree comparable to the parental cell line; this coincided with the cells regaining the ability to accumulate ROS and reduced expression of anti-apoptotic factors and the antioxidant SOD2. We suggest that our findings provide a unique IBC model system for gaining an understanding of acquired therapeutic resistance and the effect of redox adaptation on anti-cancer drug efficacy.

Polyamines and cancer: implications for chemotherapy and chemoprevention

Polyamines are small organic cations that are essential for normal cell growth and development in eukaryotes. Under normal physiological conditions, intracellular polyamine concentrations are tightly regulated through a dynamic network of biosynthetic and catabolic enzymes, and a poorly characterised transport system. This precise regulation ensures that the intracellular concentration of polyamines is maintained within strictly controlled limits. It has frequently been observed that the metabolism of, and the requirement for, polyamines in tumours is frequently dysregulated. Elevated levels of polyamines have been associated with breast, colon, lung, prostate and skin cancers, and altered levels of rate-limiting enzymes in both biosynthesis and catabolism have been observed. Based on these observations and the absolute requirement for polyamines in tumour growth, the polyamine pathway is a rational target for chemoprevention and chemotherapeutics. Here we describe the recent advances made in the polyamine field and focus on the roles of polyamines and polyamine metabolism in neoplasia through a discussion of the current animal models for the polyamine pathway, chemotherapeutic strategies that target the polyamine pathway, chemotherapeutic clinical trials for polyamine pathway-specific drugs and ongoing clinical trials targeting polyamine biosynthesis.

Lectin chromatography/mass spectrometry discovery workflow identifies putative biomarkers of aggressive breast cancers

We used a lectin chromatography/MS-based approach to screen conditioned medium from a panel of luminal (less aggressive) and triple negative (more aggressive) breast cancer cell lines (n=5/subtype). The samples were fractionated using the lectins Aleuria aurantia (AAL) and Sambucus nigra agglutinin (SNA), which recognize fucose and sialic acid, respectively. The bound fractions were enzymatically N-deglycosylated and analyzed by LC-MS/MS. In total, we identified 533 glycoproteins, ∼90% of which were components of the cell surface or extracellular matrix. We observed 1011 glycosites, 100 of which were solely detected in ≥3 triple negative lines. Statistical analyses suggested that a number of these glycosites were triple negative-specific and thus potential biomarkers for this tumor subtype. An analysis of RNaseq data revealed that approximately half of the mRNAs encoding the protein scaffolds that carried potential biomarker glycosites were up-regulated in triple negative vs luminal cell lines, and that a number of genes encoding fucosyl- or sialyltransferases were differentially expressed between the two subtypes, suggesting that alterations in glycosylation may also drive candidate identification. Notably, the glycoproteins from which these putative biomarker candidates were derived are involved in cancer-related processes. Thus, they may represent novel therapeutic targets for this aggressive tumor subtype.

Subtype and pathway specific responses to anticancer compounds in breast cancer

Breast cancers are comprised of molecularly distinct subtypes that may respond differently to pathway-targeted therapies now under development. Collections of breast cancer cell lines mirror many of the molecular subtypes and pathways found in tumors, suggesting that treatment of cell lines with candidate therapeutic compounds can guide identification of associations between molecular subtypes, pathways, and drug response. In a test of 77 therapeutic compounds, nearly all drugs showed differential responses across these cell lines, and approximately one third showed subtype-, pathway-, and/or genomic aberration-specific responses. These observations suggest mechanisms of response and resistance and may inform efforts to develop molecular assays that predict clinical response.

BikDD eliminates breast cancer initiating cells and synergizes with lapatinib for breast cancer treatment

Breast cancer initiating cells (BCICs), which can fully recapitulate the tumor origin and are often resistant to chemo- and radiotherapy, are currently considered as a major obstacle for breast cancer treatment. Here, we show that BIKDD, a constitutively active mutant form of proapoptotic gene, BIK, effectively induces apoptosis of breast cancer cells and synergizes with lapatinib. Most importantly, BikDD significantly reduces BCICs through co-antagonism of its binding partners Bcl-2, Bcl-xL, and Mcl-1, suggesting a potential therapeutic strategy targeting BCICs. Furthermore, we developed a cancer-specific targeting approach for breast cancer that selectively expresses BikDD in breast cancer cells including BCICs, and demonstrated its potent antitumor activity and synergism with lapatinib in vitro and in vivo.

Initial testing (stage 1) of the polyamine analog PG11047 by the pediatric preclinical testing program

BACKGROUND

PG11047 is a novel conformationally restricted analog of the natural polyamine, spermine that lowers cellular endogenous polyamine levels and competitively inhibits natural polyamine functions leading to cancer cell growth inhibition. The activity of PG11047 was evaluated against the PPTP's in vitro and in vivo panels.

PROCEDURES

PG11047 was evaluated against the PPTP in vitro panel using 96 hr exposure at concentrations ranging from 10 nM to 100 µM. It was tested against the PPTP in vivo panels at a dose of 100 mg/kg administered by the intraperitoneal route weekly for 6 weeks.

RESULTS

In vitro PG11047 demonstrated a concentration-response pattern consistent with cytostatic activity. The median EC(50) for PG11047 was 71 nM. Cell lines of the Ewing sarcoma panel had a lower median EC(50) value compared to the remaining cell lines in the panel, while cell lines of the neuroblastoma panel had a higher median EC(50) value. In vivo PG11047 induced significant differences in EFS distribution compared to control in 5 of 32 (15.6%) of the evaluable solid tumor xenografts and in 0 of 7 (0%) of the evaluable ALL xenografts. The single case of tumor regression occurred in an ependymoma xenograft.

CONCLUSIONS

Further pediatric development of PG11047 will require better defining a target population and identifying combinations for which there is a tumor-selective cytotoxic effect. The regression observed for an ependymoma xenograft and the exquisite sensitivity of some Ewing sarcoma cell lines to the antiproliferative effects of PG11047 provide leads for further preclinical investigations.

Reduction of the putative CD44+CD24- breast cancer stem cell population by targeting the polyamine metabolic pathway with PG11047

Cancer stem cells (CSCs) are considered to be of particular concern in cancer as they possess inherent properties of self-renewal and differentiation, along with expressing certain genes related to a mesenchymal phenotype. These features favour the promotion of tumour recurrence and metastasis in cancer patients. Thus, the optimal chemotherapeutic treatment should target the CSC population, either by killing these cells and/or by inducing their transition to a more differentiated epithelial-like phenotype. Experiments were carried out on the trastuzumab-resistant human epidermal growth factor receptor 2-overexpressing breast cancer cell line JIMT-1 to unravel the chemotherapeutic effects of the polyamine analogue [1N,12N]bis(ethyl)-cis-6,7-dehydrospermine (PG11047) and of the polyamine biosynthetic inhibitor 2-difluoromethylornithine (DFMO) on the CD44+CD24- CSC population. Furthermore, effects on the properties of self-renewal and epithelial/mesenchymal markers were also investigated. Treatment with PG11047 reduced the CD44+CD24- subpopulation of JIMT-1 cells by approximately 50%, inhibited and/or reduced self-renewal capability of the CSC population, decreased cell motility and induced expression of mesenchymal to epithelial transition-associated proteins that are involved in promoting an epithelial phenotype. By contrast, DFMO slightly increased the CD44+CD24- subpopulation, increased cell motility and the level of mesenchymal-related proteins. DFMO treatment reduced the self-renewal capability of the CSC population. Both PG11047 and DFMO reduced the expression of the human epidermal growth factor receptor 2 protein, which is correlated to malignancy and resistance to trastuzumab in JIMT-1 cells. Our findings indicate that treatment with PG11047 targeted the CSC population by interfering with several stem cell-related properties, such as self-renewal, differentiation, motility and the mesenchymal phenotype.

Cell line-based platforms to evaluate the therapeutic efficacy of candidate anticancer agents

Efforts to discover new cancer drugs and predict their clinical activity are limited by the fact that laboratory models to test drug efficacy do not faithfully recapitulate this complex disease. One important model system for evaluating candidate anticancer agents is human tumour-derived cell lines. Although cultured cancer cells can exhibit distinct properties compared with their naturally growing counterparts, recent technologies that facilitate the parallel analysis of large panels of such lines, together with genomic technologies that define their genetic constitution, have revitalized efforts to use cancer cell lines to assess the clinical utility of new investigational cancer drugs and to discover predictive biomarkers.

Integrating complex genomic datasets and tumour cell sensitivity profiles to address a 'simple' question: which patients should get this drug?

It is becoming increasingly apparent that cancer drug therapies can only reach their full potential through appropriate patient selection. Matching drugs and cancer patients has proven to be a complex challenge, due in large part to the substantial molecular heterogeneity inherent to human cancers. This is not only a major hurdle to the improvement of the use of current treatments but also for the development of novel therapies and the ability to steer them to the relevant clinical indications. In this commentary we discuss recent studies from Kuo et al., published this month in BMC Medicine, in which they used a panel of cancer cell lines as a model for capturing patient heterogeneity at the genomic and proteomic level in order to identify potential biomarkers for predicting the clinical activity of a novel candidate chemotherapeutic across a patient population. The findings highlight the ability of a 'systems approach' to develop a better understanding of the properties of novel candidate therapeutics and to guide clinical testing and application.See the associated research paper by Kuo et al: http://www.biomedcentral.com/1741-7015/7/77.