The exact clinical significance of EGFR mutation status in NSCLC at the time of initial diagnosis remains disputable. The gene expression module in NSCLC for chemotherapy outcome prediction needs to be developed. We analyzed 56 patients with NSCLC received chemotherapy either with (n=20) or without EGFR-TKIs (n=36) between 2008 and 2012 in China. EGFR mutation test and gene expression profiling were performed in samples obtained before medication treatment by liquidchip platform. Significant association (P = 0.028) was seen between EGFR mutation status before first-line chemotherapy and EGFR-TKIs treatment outcomes, which even can be found from the status before second- or third-line treatment. A14-gene expression profiling had been studied. Patients with low mRNA expression of ERCC1 or TYMS preferred higher DCR to cisplatin and pemetrexed than those with high expression (P = 0.39 and P= 0.11). Highly co-expression of TUBB3 and STMN1 gene has associated with the resistance to antimicrotubule drugs (P = 0.03). Our data suggest the EGFR mutations status, even at the time of initial diagnosis, is predictive of outcomes of TKIs treatment after chemotherapy. The mRNA expression profiling investigated in this study has a predictive value in NSCLC treatment, but further research with expanded samples is still required.

BACKGROUND: Precision (Personalized) medicine has the potential to revolutionize patient health care especially for many cancers where the fundamental disease etiology remains either elusive or has no available therapy. Here we outline a study in alveolar rhabdomyosarcoma, in which we use gene expression profiling and a series of drug prediction algorithms combined with a matched patient-derived xenograft (PDX) model to test bioinformatically predicted therapies.
PROCEDURE: A PDX model was developed from a patient biopsy and a number of drugs identified using gene expression analysis in combination with drug prediction algorithms. Drugs chosen from each of the predictive methodologies, along with the patient's standard-of-care therapy (ICE-T), were tested in vivo in the PDX tumor. A second study was initiated using the tumors that re-grew following the ICE-T treatment. Further expression analysis identified additional therapies with potential anti-tumor efficacy.
RESULTS: A number of the predicted therapies were found to be active against the tumors in particular BGJ398 (FGFR2) and ICE-T. Re-transplanted ICE-T treated tumorgrafts demonstrated a decreased response to ICE-T recapitulating the patient's refractory disease. Gene expression profiling of the ICE-T treated tumorgrafts identified cytarabine (SLC29A1) as a potential therapy, which was shown, along with BGJ398, to be highly active in vivo.
CONCLUSIONS: This study illustrates that PDX models are suitable surrogates for testing potential therapeutic strategies based on gene expression analysis, modeling clinical drug resistance and hold the potential to assist in guiding prospective patient care.

MOTIVATION: Metastasis prediction is a well-known problem in breast cancer research. As breast cancer is a complex and heterogeneous disease with many molecular subtypes, predictive models trained for one cohort often perform poorly on other cohorts, and a combined model may be suboptimal for individual patients. Furthermore, attempting to develop subtype-specific models is hindered by the ambiguity and stereotypical definitions of subtypes.
RESULTS: Here, we propose a personalized approach by relaxing the definition of breast cancer subtypes. We assume that each patient belongs to a distinct subtype, defined implicitly by a set of patients with similar molecular characteristics, and construct a different predictive model for each patient, using as training data, only the patients defining the subtype. To increase robustness, we also develop a committee-based prediction method by pooling together multiple personalized models. Using both intra- and inter-dataset validations, we show that our approach can significantly improve the prediction accuracy of breast cancer metastasis compared with several popular approaches, especially on those hard-to-learn cases. Furthermore, we find that breast cancer patients belonging to different canonical subtypes tend to have different predictive models and gene signatures, suggesting that metastasis in different canonical subtypes are likely governed by different molecular mechanisms.

Aggressive tumors such as epithelial ovarian cancer (EOC) are highly heterogeneous in their therapeutic response, making it difficult to improve overall response by using drugs in unselected patients. The goal of this study was to retrospectively, but independently, examine whether biomarker-based personalized chemotherapy selection could improve survival of EOC patients. Using in vitro drug sensitivity and patient clinical outcome data, we have developed co-expression extrapolation (COXEN) biomarker models for predicting patient response to three standard chemotherapy drugs used to treat advanced EOC: paclitaxel, cyclophosphamide, and topotecan, for which sufficient patient data were available for our modeling and independent validation. Four different cohorts of 783 EOC patients were used in our study, including two cohorts of 499 patients for independent validation. The COXEN predictors for the three drugs independently showed high prediction both for patient short-term therapeutic response and long-term survival for recurrent EOC. We then examined the potential clinical benefit of the simultaneous use of the three drug predictors for a large diverse EOC cohort in a prospective manner, finding that the median overall survival was 21 months longer for recurrent EOC patients who were treated with the predicted most effective chemotherapies. Survival improvement was greater for platinum-sensitive patients if they were treated with the predicted most beneficial drugs. Following the FDA guidelines for diagnostic prediction analysis, our study has retrospectively, yet independently, showed a potential for biomarker-based personalized chemotherapy selection to significantly improve survival of patients in the heterogeneous EOC population when using standard chemotherapies.

OBJECTIVE: To explore genes of the killer-cell immunoglobulin-like receptor (KIR) and of the HLA ligand and their relationship with the outcome of metastatic colorectal cancer (mCRC) patients treated with first-line 5-fluorouracil, leucovorin, and irinotecan (FOLFIRI).
METHODS: A total of 224 mCRC patients were screened for KIR/HLA typing. The determination of the KIR/HLA combinations was based upon the gene content and variants. Genetic associations with complete response (CR), time to progression (TTP) and overall survival (OS) were evaluated by calculating odds and hazard ratios. Multivariate modeling with prognostic covariates was also performed.
RESULTS: For CR, the presence of KIR2DL5A, 2DS5, 2DS1, 3DS1, and KIR3DS1/HLA-Bw4-I80 was associated with increased CR rates, with median ORs ranging from 2.1 to 4.3, while the absence of KIR2DS4 and 3DL1 was associated with increased CR rates (OR 3.1). After univariate analysis, patients that underwent resective surgery of tumor, absence of KIR2DS5, and presence of KIR3DL1/HLA-Bw4-I80 showed a significant better OS (HR 1.5 to 2.8). Multivariate analysis identified as parameters independently related to OS the type of treatment (surgery; HR 2.0) and KIR3DL1/HLA-Bw4-I80 genotype (HR for T-I80 2.7 and for no functional KIR/HLA interaction 1.8). For TTP, no association with KIR/HLA genes was observed.
CONCLUSION: This study, for the first time, evidences that the genotyping for KIR-HLA pairs are found predictive markers associated with complete response and improves overall survival prediction of FOLFIRI treatment response in metastatic colorectal cancer. These results suggest a role of the KIR/HLA system in patient outcome, and guide new research on the immunogenetics of mCRC through mechanistic studies and clinical validation.

The large amount of data on cancer genome research has contributed to our understanding of cancer biology. Indeed, the genomics approach has a strong advantage for analyzing multi-factorial and complicated problems, such as cancer. It is time to think about the actual usage of cancer genomics in the clinical field. The clinical cancer field has lots of unmet needs in the management of cancer patients, which has been defined in the pre-genomic era. Unmet clinical needs are not well known to bioinformaticians and even non-clinician cancer Professors. A personalized approach in the clinical field will bring potential additional challenges to cancer genomics, because most data to now have been population-based rather than individual-based. We can maximize the use of cancer genomics in the clinical field if cancer Professors, bioinformaticians, and clinicians think and work together in solving unmet clinical needs. In this review, we present one imaginary case of a cancer patient, with which we can think about unmet clinical needs to solve with cancer genomics in the diagnosis, prediction of prognosis, monitoring the status of cancer, and personalized treatment decision.

Recent advances in human genomics and biotechnologies have profound impacts on medical research and clinical practice. Individual genomic information, including DNA sequences and gene expression profiles, can be used for prediction, prevention, diagnosis, and treatment for many complex diseases. Personalized medicine attempts to tailor medical care to individual patients by incorporating their genomic information. In a case of pancreatic cancer, the fourth leading cause of cancer death in the United States, alteration in many genes as well as molecular profiles in blood, pancreas tissue, and pancreas juice has recently been discovered to be closely associated with tumorigenesis or prognosis of the cancer. This review aims to summarize recent advances of important genes, proteins, and microRNAs that play a critical role in the pathogenesis of pancreatic cancer, and to provide implications for personalized medicine in pancreatic cancer.

Gastric cancer is the second leading cause of cancer-related deaths worldwide. In advanced and metastatic gastric cancer, the conventional chemotherapy with limited efficacy shows an overall survival period of about 10 months. Patient specific and effective treatments known as personalized cancer therapy is of significant importance. Advances in high-throughput technologies such as microarray and next generation sequencing for genes, protein expression profiles and oncogenic signaling pathways have reinforced the discovery of treatment targets and personalized treatments. However, there are numerous challenges from cancer target discoveries to practical clinical benefits. Although there is a flood of biomarkers and target agents, only a minority of patients are tested and treated accordingly. Numerous molecular target agents have been under investigation for gastric cancer. Currently, targets for gastric cancer include the epidermal growth factor receptor family, mesenchymal-epithelial transition factor axis, and the phosphatidylinositol 3-kinase-AKT-mammalian target of rapamycin pathways. Deeper insights of molecular characteristics for gastric cancer has enabled the molecular classification of gastric cancer, the diagnosis of gastric cancer, the prediction of prognosis, the recognition of gastric cancer driver genes, and the discovery of potential therapeutic targets. Not only have we deeper insights for the molecular diversity of gastric cancer, but we have also prospected both affirmative potentials and hurdles to molecular diagnostics. New paradigm of transdisciplinary team science, which is composed of innovative explorations and clinical investigations of oncologists, geneticists, pathologists, biologists, and bio-informaticians, is mandatory to recognize personalized target therapy.

PURPOSE: Personalized medicine is predicated on the concept of identifying subgroups of a common disease for better treatment. Identifying biomarkers that predict disease subtypes has been a major focus of biomedical science. In the era of genome-wide profiling, there is controversy as to the optimal number of genes as an input of a feature selection algorithm for survival modeling.
PATIENTS AND METHODS: The expression profiles and outcomes of 544 patients were retrieved from The Cancer Genome Atlas. We compared four different survival prediction methods: (1) 1-nearest neighbor (1-NN) survival prediction method; (2) random patient selection method and a Cox-based regression method with nested cross-validation; (3) least absolute shrinkage and selection operator (LASSO) optimization using whole-genome gene expression profiles; or (4) gene expression profiles of cancer pathway genes.
RESULTS: The 1-NN method performed better than the random patient selection method in terms of survival predictions, although it does not include a feature selection step. The Cox-based regression method with LASSO optimization using whole-genome gene expression data demonstrated higher survival prediction power than the 1-NN method, but was outperformed by the same method when using gene expression profiles of cancer pathway genes alone.
CONCLUSION: The 1-NN survival prediction method may require more patients for better performance, even when omitting censored data. Using preexisting biological knowledge for survival prediction is reasonable as a means to understand the biological system of a cancer, unless the analysis goal is to identify completely unknown genes relevant to cancer biology.

Histopathology, the examination of an architecturally artefactual, two-dimensional and static image remains a potent tool allowing diagnosis and empirical expectation of prognosis. Considerable optimism exists that the advent of molecular genetic testing and other biomarker strategies will improve or even replace this ancient technology. A number of biomarkers already add considerable value for prediction of whether a treatment will work. In this short review we argue that a systems medicine approach to pathology will not seek to replace traditional pathology, but rather augment it. Systems approaches need to incorporate quantitative morphological, protein, mRNA and DNA data. A significant challenge for clinical implementation of systems pathology is how to optimize information available from tissue, which is frequently sub-optimal in quality and amount, and yet generate useful predictive models that work. The transition of histopathology to systems pathophysiology and the use of multiscale data sets usher in a new era in diagnosis, prognosis and prediction based on the analysis of human tissue.

EGFR mutation-induced drug resistance has significantly impaired the potency of small molecule tyrosine kinase inhibitors in lung cancer treatment. Computational approaches can provide powerful and efficient techniques in the investigation of drug resistance. In our work, the EGFR mutation feature is characterized by the energy components of binding free energy (concerning the mutant-inhibitor complex), and we combine it with specific personal features for 168 clinical subjects to construct a personalized drug resistance prediction model. The 3D structure of an EGFR mutant is computationally predicted from its protein sequence, after which the dynamics of the bound mutant-inhibitor complex is simulated via AMBER and the binding free energy of the complex is calculated based on the dynamics. The utilization of extreme learning machines and leave-one-out cross-validation promises a successful identification of resistant subjects with high accuracy. Overall, our study demonstrates advantages in the development of personalized medicine/therapy design and innovative drug discovery.

Resistance to chemotherapy and molecularly targeted therapies is a major problem facing current cancer research. The mechanisms of resistance to 'classical' cytotoxic chemotherapeutics and to therapies that are designed to be selective for specific molecular targets share many features, such as alterations in the drug target, activation of prosurvival pathways and ineffective induction of cell death. With the increasing arsenal of anticancer agents, improving preclinical models and the advent of powerful high-throughput screening techniques, there are now unprecedented opportunities to understand and overcome drug resistance through the clinical assessment of rational therapeutic drug combinations and the use of predictive biomarkers to enable patient stratification.

Drug resistance, de novo and acquired, pervades cellular signaling networks (SNs) from one signaling motif to another as a result of cancer progression and/or drug intervention. This resistance is one of the key determinants of efficacy in targeted anti-cancer drug therapy. Although poorly understood, drug resistance is already being addressed in combination therapy by selecting drug targets where SN sensitivity increases due to combination components or as a result of de novo or acquired mutations. Additionally, successive drug combinations have shown low resistance potential. To promote a rational, systematic development of combination therapies, it is necessary to establish the underlying mechanisms that drive the advantages of combination therapies, and design methods to determine drug targets for combination regimens. Based on a joint systems analysis of cellular SN response and its sensitivity to drug action and oncogenic mutations, we describe an in silico method to analyze the targets of drug combinations. Our method explores mechanisms of sensitizing the SN through a combination of two drugs targeting vertical signaling pathways. We propose a paradigm of SN response customization by one drug to both maximize the effect of another drug in combination and promote a robust therapeutic response against oncogenic mutations. The method was applied to customize the response of the ErbB/PI3K/PTEN/AKT pathway by combination of drugs targeting HER2 receptors and proteins in the down-stream pathway. The results of a computational experiment showed that the modification of the SN response from hyperbolic to smooth sigmoid response by manipulation of two drugs in combination leads to greater robustness in therapeutic response against oncogenic mutations determining cancer heterogeneity. The application of this method in drug combination co-development suggests a combined evaluation of inhibition effects together with the capability of drug combinations to suppress resistance mechanisms before they become clinically manifest.

Several tumour types are sensitive to deactivation of just one or very few genes that are constantly active in the cancer cells, a phenomenon that is termed 'oncogene addiction'. Drugs that target the products of those oncogenes can yield a temporary relief, and even complete remission. Unfortunately, many patients receiving oncogene-targeted therapies relapse on treatment. This often happens due to somatic mutations in the oncogene ('resistance mutations'). 'Compound mutations', which in the context of cancer drug resistance are defined as two or more mutations of the drug target in the same clone may lead to enhanced resistance against the most selective inhibitors. Here, it is shown that the vast majority of the resistance mutations occurring in cancer patients treated with tyrosin kinase inhibitors aimed at three different proteins follow an evolutionary pathway. Using bioinformatic analysis tools, it is found that the drug-resistance mutations in the tyrosine kinase domains of Abl1, ALK and exons 20 and 21 of EGFR favour transformations to residues that can be identified in similar positions in evolutionary related proteins. The results demonstrate that evolutionary pressure shapes the mutational landscape in the case of drug-resistance somatic mutations. The constraints on the mutational landscape suggest that it may be possible to counter single drug-resistance point mutations. The observation of relatively many resistance mutations in Abl1, but not in the other genes, is explained by the fact that mutations in Abl1 tend to be biochemically conservative, whereas mutations in EGFR and ALK tend to be radical. Analysis of Abl1 compound mutations suggests that such mutations are more prevalent than hitherto reported and may be more difficult to counter. This supports the notion that such mutations may provide an escape route for targeted cancer drug resistance.

Innate resistance to various therapeutic interventions is a hallmark of cancer. In recent years, acquired resistance has emerged as a daunting challenge to targeted cancer therapy, which abolishes the efficacy of otherwise successful targeting drugs. Cancer cells gain the resistance property through a variety of mechanisms in primary and metastatic cancers, involving cellular intrinsic and extrinsic factors. Increasing evidence suggests that the mammalian stress response gene sirtuin 1 (SIRT1) plays a critical role in multiple aspects of cancer drug resistance. SIRT1 decreases drug penetration, confers proliferation and antiapoptotic survival advantages to cancer cells, facilitates acquired resistance through genetic mutations, promotes the survival of cancer stem cells, and changes the tumor microenvironment for resistance in cell-autonomous and -nonautonomous manners. This article provides an overview of research advances in the roles of SIRT1 in cancer drug resistance and highlights the prospect of targeting SIRT1 as a new strategy to overcome cancer drug resistance and improve therapeutic outcomes.

Several tumour types are sensitive to deactivation of just one or very few genes that are constantly active in the cancer cells, a phenomenon that is termed 'oncogene addiction'. Drugs that target the products of those oncogenes can yield a temporary relief, and even complete remission. Unfortunately, many patients receiving oncogene-targeted therapies relapse on treatment. This often happens due to somatic mutations in the oncogene ('resistance mutations'). 'Compound mutations', which in the context of cancer drug resistance are defined as two or more mutations of the drug target in the same clone may lead to enhanced resistance against the most selective inhibitors. Here, it is shown that the vast majority of the resistance mutations occurring in cancer patients treated with tyrosin kinase inhibitors aimed at three different proteins follow an evolutionary pathway. Using bioinformatic analysis tools, it is found that the drug-resistance mutations in the tyrosine kinase domains of Abl1, ALK and exons 20 and 21 of EGFR favour transformations to residues that can be identified in similar positions in evolutionary related proteins. The results demonstrate that evolutionary pressure shapes the mutational landscape in the case of drug-resistance somatic mutations. The constraints on the mutational landscape suggest that it may be possible to counter single drug-resistance point mutations. The observation of relatively many resistance mutations in Abl1, but not in the other genes, is explained by the fact that mutations in Abl1 tend to be biochemically conservative, whereas mutations in EGFR and ALK tend to be radical. Analysis of Abl1 compound mutations suggests that such mutations are more prevalent than hitherto reported and may be more difficult to counter. This supports the notion that such mutations may provide an escape route for targeted cancer drug resistance.

Innate resistance to various therapeutic interventions is a hallmark of cancer. In recent years, acquired resistance has emerged as a daunting challenge to targeted cancer therapy, which abolishes the efficacy of otherwise successful targeting drugs. Cancer cells gain the resistance property through a variety of mechanisms in primary and metastatic cancers, involving cellular intrinsic and extrinsic factors. Increasing evidence suggests that the mammalian stress response gene sirtuin 1 (SIRT1) plays a critical role in multiple aspects of cancer drug resistance. SIRT1 decreases drug penetration, confers proliferation and antiapoptotic survival advantages to cancer cells, facilitates acquired resistance through genetic mutations, promotes the survival of cancer stem cells, and changes the tumor microenvironment for resistance in cell-autonomous and -nonautonomous manners. This article provides an overview of research advances in the roles of SIRT1 in cancer drug resistance and highlights the prospect of targeting SIRT1 as a new strategy to overcome cancer drug resistance and improve therapeutic outcomes.

Intrinsic and acquired drug resistance of tumor cells still causes the failure of treatment regimens in advanced human cancers. It may be driven by intrinsic tumor cells features, or may also arise from micro environmental influences. Hypoxia is a microenvironment feature associated with the aggressiveness and metastasizing ability of human solid cancers. Hypoxic cancer cells overexpress Carbonic Anhydrase IX (CA IX). CA IX ensures a favorable tumor intracellular pH, while contributing to stromal acidosis, which facilitates tumor invasion and metastasis. The overexpression of CA IX is considered an epiphenomenon of the presence of hypoxic, aggressive tumor cells. Recently, a relationship between CA IX overexpression and the cancer stem cells (CSCs) population has been hypothesized. CSCs are strictly regulated by tumor hypoxia and drive a major non-mutational mechanism of cancer drug-resistance. We reviewed the current data concerning the role of CA IX overexpression in human malignancies, extending such information to the expression of the stem cells markers CD44 and nestin in solid cancers, to explore their relationship with the biological behavior of tumors. CA IX is heavily expressed in advanced tumors. A positive trend of correlation between CA IX overexpression, tumor stage/grade and poor outcome emerged. Moreover, stromal CA IX expression was associated with adverse events occurrence, maybe signaling the direct action of CA IX in directing the mesenchymal changes that favor tumor invasion; in addition, membranous/cytoplasmic co-overexpression of CA IX and stem cells markers were found in several aggressive tumors. This suggests that CA IX targeting could indirectly deplete CSCs and counteract resistance of solid cancers in the clinical setting.

Drug resistance is a common cause of treatment failure in cancer patients and encompasses a multitude of different mechanisms. The aim of the present study was to identify drugs effective on multidrug resistant cells.

MiR-34a, a direct target of p53, has been shown to target several molecules associated with the cell cycle and cell survival pathways, and its dysregulation is implicated in cancer drug resistance or sensitivity in several human cancers. However, the correlation between miR-34a expression and chemoresistance has not been explored in HCC. In this study, we confirmed that miR-34a was significantly down-regulated in HCC tissues and HCC cell lines by qRT-PCR. HCC tissues with lower miR-34a expression displayed higher expression of Bcl-2 protein than those with high expression of miR-34a; therefore, an inverse correlation is evident between the miR-34a level and Bcl-2 expression. Moreover, patients with lower miR-34a expression had significantly poorer overall survival. Bioinformatics and luciferase reporter assays revealed that miR-34a binds the 3'-UTR of the Bcl-2 mRNA and represses its translation. Western blotting analysis and qRT-PCR confirmed that Bcl-2 is inhibited by miR-34a overexpression. Functional analyses indicated that the restoration of miR-34a reduced cell viability, promoted cell apoptosis and potentiated sorafenib-induced apoptosis and toxicity in HCC cell lines by inhibiting Bcl-2 expression. This study is the first to demonstrate that miR-34a induces sensitivity to the anti-tumor effect of sorafenib in human HCC cells, suggesting a potential role of miR-34a in the treatment of HCC.

Resistance to cisplatin-based chemotherapy is responsible for the majority of deaths from head and neck squamous cell carcinoma (HNSCC). In this study, using genome-wide gene expression analysis to investigate potential molecular mediators of HNSCC chemoresistance, we identified SERPINB2, a known inhibitor of extracellular serine proteinase urokinase-type plasminogen activator (uPA), as an important candidate. Whereas SERPINB2 is known to function as a suppressor of uPA molecular cascades, many of which play important roles in tumor invasion and metastasis, a role for SERPINB2 in cancer drug resistance has not been examined. By using quantitative real-time PCR and Western blot analysis, we determined that SERPINB2 mRNA and protein levels correlated with chemoresistance in HNSCC cell lines, and significantly lower SERPINB2 expression levels were observed in two cisplatin resistant HNSCC subclones compared to their isogenic drug-sensitive parental lines. Immunohistochemical analysis of HNSCC tumor tissues from patients treated with neoadjuvant cisplatin-based chemotherapy (n = 67 cases) revealed a significant association between SERPINB2 protein levels, tumor differentiation and patient relapse. Moreover, SERPINB2 down-regulation was a strong predictor of reduced overall survival in patients with HNSCC who received cisplatin-based chemotherapy (P = 0.001, log rank test). Studies using either siRNA-mediated down-regulation or forced over-expression of SERPINB2 in HNSCC cell lines confirmed a functional role for SERPINB2 in drug resistance. The findings were further supported using chemical inhibitors of STAT3 activity (a downstream effecter of uPAR signaling pathway), showing that STAT3 suppression altered HNSCC cell line cisplatin sensitivity. This is the first report on a role for SERPINB2 in acquired resistance to cisplatin in patients with HNSCC. © 2013 Wiley Periodicals, Inc.

We have developed a rapid, simple and reliable, antibody-based flow cytometry assay for the quantitative determination of membrane proteins in human erythrocytes. Our method reveals significant differences between the expression levels of the wild-type ABCG2 protein and the heterozygous Q141K polymorphic variant. Moreover, we find that nonsense mutations on one allele result in a 50% reduction in the erythrocyte expression of this protein. Since ABCG2 polymorphisms are known to modify essential pharmacokinetic parameters, uric acid metabolism and cancer drug resistance, a direct determination of the erythrocyte membrane ABCG2 protein expression may provide valuable information for assessing these conditions or for devising drug treatments. Our findings suggest that erythrocyte membrane protein levels may reflect genotype-dependent tissue expression patterns. Extension of this methodology to other disease-related or pharmacologically important membrane proteins may yield new protein biomarkers for personalized diagnostics.

Over the past decade, whole genome sequencing and other 'omics' technologies have defined pathogenic driver mutations to which tumor cells are addicted. Such addictions, synthetic lethalities and other tumor vulnerabilities have yielded novel targets for a new generation of cancer drugs to treat discrete, genetically defined patient subgroups. This personalized cancer medicine strategy could eventually replace the conventional one-size-fits-all cytotoxic chemotherapy approach. However, the extraordinary intratumor genetic heterogeneity in cancers revealed by deep sequencing explains why de novo and acquired resistance arise with molecularly targeted drugs and cytotoxic chemotherapy, limiting their utility. One solution to the enduring challenge of polygenic cancer drug resistance is rational combinatorial targeted therapy.

Drug resistance continues to be a major barrier to the delivery of curative therapies in cancer. Historically, drug resistance has been associated with over-expression of drug transporters, changes in drug kinetics or amplification of drug targets. However, the emergence of resistance in patients treated with new-targeted therapies has provided new insight into the complexities underlying cancer drug resistance. Recent data now implicate intratumoural heterogeneity as a major driver of drug resistance. Single cell sequencing studies that identified multiple genetically distinct variants within human tumours clearly demonstrate the heterogeneous nature of human tumours. The major contributors to intratumoural heterogeneity are (i) genetic variation, (ii) stochastic processes, (iii) the microenvironment and (iv) cell and tissue plasticity. Each of these factors impacts on drug sensitivity. To deliver curative therapies to patients, modification of current therapeutic strategies to include methods that estimate intratumoural heterogeneity and plasticity will be essential.

One of the main reasons of cancer resistance to chemotherapeutic treatment is the presence of different ABC multidrug transporters in plasma membranes. The transporters extrude wide spectrum of anticancer agents out of cancer cells at the expense of energy derived from ATP-hydrolysis. Plant-origin polyphenolic compounds, mainly flavonoids and stilbenes or their synthetic derivatives, can modulate the main ABC transporters responsible for cancer drug resistance, including P-glycoprotein, MRP1 and BCRP. The recent studies on different resistant cancer cell lines enabled the discovery of a number of polyphenolic compounds able to reverse drug resistance in vitro and these compounds could be promising candidates for further clinical trials. The review summarizes the recent advances in the field of polyphenols interaction with ATP-binding cassette multidrug transporters. The mechanism of flavonoids interactions with the multidrug transporters and the structure-activity relationship are also discussed.

Human ABCG2 is a plasma membrane glycoprotein that provides physiological protection against xenobiotics. ABCG2 also significantly influences biodistribution of drugs through pharmacological tissue barriers and confers multidrug resistance to cancer cells. Moreover, ABCG2 is the molecular determinant of the side population that is characteristically enriched in normal and cancer stem cells. Numerous tumors depend on unregulated EGFR signaling, thus inhibition of this receptor by small molecular weight inhibitors such as gefitinib, and the novel second generation agents vandetanib, pelitinib and neratinib, is a promising therapeutic option. In the present study, we provide detailed biochemical characterization regarding the interaction of these EGFR inhibitors with ABCG2. We show that ABCG2 confers resistance to gefitinib and pelitinib, whereas the intracellular action of vandetanib and neratinib is unaltered by the presence of the transporter. At higher concentrations, however, all these EGFR inhibitors inhibit ABCG2 function, thereby promoting accumulation of ABCG2 substrate drugs. We also report enhanced expression of ABCG2 in gefitinib-resistant non-small cell lung cancer cells, suggesting potential clinical relevance of ABCG2 in acquired drug resistance. Since ABCG2 has important impact on both the pharmacological properties and anti-cancer efficiencies of drugs, our results regarding the novel EGFR inhibitors should provide useful information about their therapeutic applicability against ABCG2-expressing cancer cells depending on EGFR signaling. In addition, the finding that these EGFR inhibitors efficiently block ABCG2 function may help to design novel drug-combination therapeutic strategies.

Since cloning of the ATP-binding cassette (ABC) family member breast cancer resistance protein (BCRP/ABCG2) and its characterization as a multidrug resistance efflux transporter in 1998, BCRP has been the subject of more than two thousand scholarly articles. In normal tissues, BCRP functions as a defense mechanism against toxins and xenobiotics, with expression in the gut, bile canaliculi, placenta, blood-testis and blood-brain barriers facilitating excretion and limiting absorption of potentially toxic substrate molecules, including many cancer chemotherapeutic drugs. BCRP also plays a key role in heme and folate homeostasis, which may help normal cells survive under conditions of hypoxia. BCRP expression appears to be a characteristic of certain normal tissue stem cells termed "side population cells," which are identified on flow cytometric analysis by their ability to exclude Hoechst 33342, a BCRP substrate fluorescent dye. Hence, BCRP expression may contribute to the natural resistance and longevity of these normal stem cells. Malignant tissues can exploit the properties of BCRP to survive hypoxia and to evade exposure to chemotherapeutic drugs. Evidence is mounting that many cancers display subpopulations of stem cells that are responsible for tumor self-renewal. Such stem cells frequently manifest the "side population" phenotype characterized by expression of BCRP and other ABC transporters. Along with other factors, these transporters may contribute to the inherent resistance of these neoplasms and their failure to be cured.

Docetaxel is a chemotherapy drug to treat breast cancer, however as with many chemotherapeutic drugs resistance to docetaxel occurs in 50% of patients, and the underlying molecular mechanisms of drug resistance are not fully understood. Gene regulation through microRNAs (miRNA) has been shown to play an important role in cancer drug resistance. By directly targeting mRNA, miRNAs are able to inhibit genes that are necessary for signalling pathways or drug induced apoptosis rendering cells drug resistant. This study investigated the role of differential miRNA expression in two in vitro breast cancer cell line models (MCF-7, MDA-MB-231) of acquired docetaxel resistance. MiRNA microarray analysis identified 299 and 226 miRNAs altered in MCF-7 and MDA-MB-231 docetaxel-resistant cells, respectively. Docetaxel resistance was associated with increased expression of miR-34a and miR-141 and decreased expression of miR-7, miR-16, miR-30a, miR-125a-5p, miR-126. Computational target prediction revealed eight candidate genes targeted by these miRNAs. Quantitative PCR and western analysis confirmed decreased expression of two genes, BCL-2 and CCND1, in docetaxel-resistant cells, which are both targeted by miR-34a. Modulation of miR-34a expression was correlated with BCL-2 and cyclin D1 protein expression changes and a direct interaction of miR-34a with BCL-2 was shown by luciferase assay. Inhibition of miR-34a enhanced response to docetaxel in MCF-7 docetaxel-resistant cells, whereas overexpression of miR-34a conferred resistance in MCF-7 docetaxel-sensitive cells. This study is the first to show differences in miRNA expression, in particular, increased expression of miR-34a in an acquired model of docetaxel resistance in breast cancer. This serves as a mechanism of acquired docetaxel resistance in these cells, possibly through direct interactions with BCL-2 and CCND1, therefore presenting a potential therapeutic target for the treatment of docetaxel-resistant breast cancer.

A highly sensitive and selective liquid chromatography tandem mass spectrometry (LC-MS/MS) method has been developed to quantify cellular levels of the tyrosine kinase inhibitors (TKIs) dasatinib (Sprycel) and lapatinib (Tykerb, Tyverb). Cellular samples were extracted with a tert-butyl methyl ether:acetonitrile (3:1, v/v):1 M ammonium formate pH 3.5 (8:1, v/v) mixture. Separation was achieved on a Hyperclone BDS C18 (150 mm x 2.0 mm 3 microm) column with isocratic elution using a mobile phase of acetonitirile-10 mM ammonium formate, pH 4 (54:46, v/v), at a flow rate of 0.2 mL/min. The TKIs were quantified using a triple quadrupole mass spectrometer which was operated in multi-reaction-monitoring mode employing positive electrospray ionisation. The limit of detection and limit of quantification for lapatinib was determined to be 15 and 31 pg on column, respectively. The limit of detection and quantification for dasatinib was 3 and 15 pg on column, respectively. The method allowed for sensitive and accurate determination of cellular levels of dasatinib and lapatinib. In addition, we examined the potential for this method to be utilised to quantitate other TKIs, using gefitinib, erlotinib, imatinib and sorafenib as examples. In principle, these agents were also quantifiable by this method, however, no drug specific validation studies were undertaken with these TKIs. The data indicates that in the cancer cell-line model, DLKP, significantly more lapatinib accumulates in cells in comparison to dasatinib. Additionally, over-expression of the membrane protein drug transporter, P-glycoprotein (P-gp) a common cancer drug resistance mechanism, greatly reduces the cellular accumulation of dasatinib but not of lapatinib.