Multidrug resistance-linked gene signature predicts overall survival of patients with primary ovarian serous carcinoma

Prediction of response to promising first-line chemotherapy in ovarian cancer patients with residual peritoneal tumors: practical biomarkers and robust multiplex models

BACKGROUND

Platinum/taxane (TC) chemotherapy with debulking surgery stays the mainstay of the treatment in ovarian cancer patients with peritoneal metastasis, and recently its novel modality, intraperitoneal carboplatin with dose-dense paclitaxel (ddTCip), was shown to have greater therapeutic impact. Nevertheless, the response varies among patients and consequent recurrence, or relapse often occurs. Discovery of therapeutic response predictor to ddTCip and/or TC therapy is eagerly awaited to improve the treatment outcome.

METHODS

Using datasets in 76 participants in our ddTCip study and published databases on patients received TC therapy, we first validated a total of 75 previously suggested markers, sought out more active biomarkers through the association analyses of genome-wide transcriptome and genotyping data with progression-free survival (PFS) and adverse events, and then developed multiplex statistical prediction models for PFS and toxicity by mainly using multiple regression analysis and the classification and regression tree (CART) algorithm.

RESULTS

The association analyses revealed that SPINK1 could be a possible biomarker of ddTCip efficacy, while ABCB1 rs1045642 and ERCC1 rs11615 would be a predictor of hematologic toxicity and peripheral neuropathy, respectively. Multiple regression analyses and CART algorithm finally provided a potent efficacy prediction model using 5 gene expression data and robust multiplex toxicity prediction models-CART models using a total of 4 genotype combinations and multiple regression models using 15 polymorphisms on 12 genes.

CONCLUSION

Biomarkers and multiplex models composed here could work well in the response prediction of ddTCip and/or TC therapy, which might contribute to realize optimal selection of the key therapy.

Overcoming drug resistance with specific nano scales to targeted therapy: Focused on metastatic cancers

Metastatic cancer, which accounts for the majority of cancer fatalities, is a difficult illness to treat. Currently used cancer treatments include radiation therapy, chemotherapy, surgery, and targeted treatment (immune, gene, and hormonal). The disadvantages of these treatments include a high risk of tumor recurrence and surgical complications that may result in permanent deformities. On the other hand, most chemotherapy drugs are small molecules, which usually have unfavorable side effects, low absorption, poor selectivity, and multi-drug resistance. Anticancer drugs can be delivered precisely to the cancer spot by encapsulating them to reduce side effects. Stimuli-responsive nanocarriers can be used for drug release at cancer sites and provide target-specific delivery. As previously stated, metastasis is the primary cause of cancer-related mortality. We have evaluated the usage of nano-medications in the treatment of some metastatic tumors.

The Role of Cytokines in Activation of Tumour-promoting Pathways and Emergence of Cancer Drug Resistance

Scientists are constantly researching and launching potential chemotherapeutic agents as an irreplaceable weapon to fight the battle against cancer. Despite remarkable advancement over the past several decades to wipe out cancer through early diagnosis, proper prevention, and timely treatment, cancer is not ready to give up and leave the battleground. It continuously tries to find some other way to give a tough fight for its survival, either by escaping from the effect of chemotherapeutic drugs or utilising its own chemical messengers like cytokines to ensure resistance. Cytokines play a significant role in cancer cell growth and progression, and the present article highlights their substantial contribution to mechanisms of resistance toward therapeutic drugs. Multiple clinical studies have even described the importance of specific cytokines released from cancer cells as well as stromal cells in conferring resistance. Herein, we discuss the different mechanism behind drug resistance and the crosstalk between tumor development and cytokines release and their contribution to showing resistance towards chemotherapeutics. As a part of this review, different approaches to cytokines profile have been identified and employed to successfully target new evolving mechanisms of resistance and their possible treatment options.

Beyond matrix stiffness: targeting force-induced cancer drug resistance

During tumor progression, mechanical abnormalities in the tumor microenvironment (TME) trigger signaling pathways in cells that activate cellular programs, resulting in tumor growth and drug resistance. In this review, we describe mechanisms of action for anti-cancer therapies and mechanotransduction programs that regulate cellular processes, including cell proliferation, apoptosis, survival and phenotype switching. We discuss how the therapeutic response is impacted by the three main mechanical TME abnormalities: high extracellular matrix (ECM) composition and stiffness; interstitial fluid pressure (IFP); and elevated mechanical forces. We also review drugs that normalize these abnormalities or block mechanosensors and mechanotransduction pathways. Finally, we discuss current challenges and perspectives for the development of new strategies targeting mechanically induced drug resistance in the clinic.

Recent Advances in Molecular and Cellular Functions of S100A10

S100A10 (p11, annexin II light chain, calpactin light chain) is a multifunctional protein with a wide range of physiological activity. S100A10 is unique among the S100 family members of proteins since it does not bind to Ca2+, despite its sequence and structural similarity. This review focuses on studies highlighting the structure, regulation, and binding partners of S100A10. The binding partners of S100A10 were collated and summarized.

Distinct mechanisms of innate and adaptive immune regulation underlie poor oncologic outcomes associated with KRAS-TP53 co-alteration in pancreatic cancer

Co-occurrent KRAS and TP53 mutations define a majority of patients with pancreatic ductal adenocarcinoma (PDAC) and define its pro-metastatic proclivity. Here, we demonstrate that KRAS-TP53 co-alteration is associated with worse survival compared with either KRAS-alone or TP53-alone altered PDAC in 245 patients with metastatic disease treated at a tertiary referral cancer center, and validate this observation in two independent molecularly annotated datasets. Compared with non-TP53 mutated KRAS-altered tumors, KRAS-TP53 co-alteration engenders disproportionately innate immune-enriched and CD8+ T-cell-excluded immune signatures. Leveraging in silico, in vitro, and in vivo models of human and murine PDAC, we discover a novel intersection between KRAS-TP53 co-altered transcriptomes, TP63-defined squamous trans-differentiation, and myeloid-cell migration into the tumor microenvironment. Comparison of single-cell transcriptomes between KRAS-TP53 co-altered and KRAS-altered/TP53WT tumors revealed cancer cell-autonomous transcriptional programs that orchestrate innate immune trafficking and function. Moreover, we uncover granulocyte-derived inflammasome activation and TNF signaling as putative paracrine mediators of innate immunoregulatory transcriptional programs in KRAS-TP53 co-altered PDAC. Immune subtyping of KRAS-TP53 co-altered PDAC reveals conflation of intratumor heterogeneity with progenitor-like stemness properties. Coalescing these distinct molecular characteristics into a KRAS-TP53 co-altered "immunoregulatory program" predicts chemoresistance in metastatic PDAC patients enrolled in the COMPASS trial, as well as worse overall survival.

The ANXA2/S100A10 Complex—Regulation of the Oncogenic Plasminogen Receptor

The generation of the serine protease plasmin is initiated by the binding of its zymogenic precursor, plasminogen, to cell surface receptors. The proteolytic activity of plasmin, generated at the cell surface, plays a crucial role in several physiological processes, including fibrinolysis, angiogenesis, wound healing, and the invasion of cells through both the basement membrane and extracellular matrix. The seminal observation by Albert Fischer that cancer cells, but not normal cells in culture, produce large amounts of plasmin formed the basis of current-day observations that plasmin generation can be hijacked by cancer cells to allow tumor development, progression, and metastasis. Thus, the cell surface plasminogen-binding receptor proteins are critical to generating plasmin proteolytic activity at the cell surface. This review focuses on one of the twelve well-described plasminogen receptors, S100A10, which, when in complex with its regulatory partner, annexin A2 (ANXA2), forms the ANXA2/S100A10 heterotetrameric complex referred to as AIIt. We present the theme that AIIt is the quintessential cellular plasminogen receptor since it regulates the formation and the destruction of plasmin. We also introduce the term oncogenic plasminogen receptor to define those plasminogen receptors directly activated during cancer progression. We then discuss the research establishing AIIt as an oncogenic plasminogen receptor-regulated during EMT and activated by oncogenes such as SRC, RAS, HIF1α, and PML-RAR and epigenetically by DNA methylation. We further discuss the evidence derived from animal models supporting the role of S100A10 in tumor progression and oncogenesis. Lastly, we describe the potential of S100A10 as a biomarker for cancer diagnosis and prognosis.

Plasmin and Plasminogen System in the Tumor Microenvironment: Implications for Cancer Diagnosis, Prognosis, and Therapy

Simple Summary

In this review, we present a detailed discussion of how the plasminogen-activation system is utilized by tumor cells in their unrelenting attack on the tissues surrounding them. Plasmin is an enzyme which is responsible for digesting several proteins that hold the tissues surrounding solid tumors together. In this process tumor cells utilize the activity of plasmin to digest tissue barriers in order to leave the tumour site and spread to other parts of the body. We specifically focus on the role of plasminogen receptor—p11 which is an important regulatory protein that facilitates the conversion of plasminogen to plasmin and by this means promotes the attack by the tumour cells on their surrounding tissues.

Abstract

The tumor microenvironment (TME) is now being widely accepted as the key contributor to a range of processes involved in cancer progression from tumor growth to metastasis and chemoresistance. The extracellular matrix (ECM) and the proteases that mediate the remodeling of the ECM form an integral part of the TME. Plasmin is a broad-spectrum, highly potent, serine protease whose activation from its precursor plasminogen is tightly regulated by the activators (uPA, uPAR, and tPA), the inhibitors (PAI-1, PAI-2), and plasminogen receptors. Collectively, this system is called the plasminogen activation system. The expression of the components of the plasminogen activation system by malignant cells and the surrounding stromal cells modulates the TME resulting in sustained cancer progression signals. In this review, we provide a detailed discussion of the roles of plasminogen activation system in tumor growth, invasion, metastasis, and chemoresistance with specific emphasis on their role in the TME. We particularly review the recent highlights of the plasminogen receptor S100A10 (p11), which is a pivotal component of the plasminogen activation system.

Taxanes in cancer treatment: Activity, chemoresistance and its overcoming

Since 1984, when paclitaxel was approved by the FDA for the treatment of advanced ovarian carcinoma, taxanes have been widely used as microtubule-targeting antitumor agents. However, their historic classification as antimitotics does not describe all their functions. Indeed, taxanes act in a complex manner, altering multiple cellular oncogenic processes including mitosis, angiogenesis, apoptosis, inflammatory response, and ROS production. On the one hand, identification of the diverse effects of taxanes on oncogenic signaling pathways provides opportunities to apply these cytotoxic drugs in a more rational manner. On the other hand, this may facilitate the development of novel treatment modalities to surmount anticancer drug resistance. In the latter respect, chemoresistance remains a major impediment which limits the efficacy of antitumor chemotherapy. Taxanes have shown impact on key molecular mechanisms including disruption of mitotic spindle, mitosis slippage and inhibition of angiogenesis. Furthermore, there is an emerging contribution of cellular processes including autophagy, oxidative stress, epigenetic alterations and microRNAs deregulation to the acquisition of taxane resistance. Hence, these two lines of findings are currently promoting a more rational and efficacious taxane application as well as development of novel molecular strategies to enhance the efficacy of taxane-based cancer treatment while overcoming drug resistance. This review provides a general and comprehensive picture on the use of taxanes in cancer treatment. In particular, we describe the history of application of taxanes in anticancer therapeutics, the synthesis of the different drugs belonging to this class of cytotoxic compounds, their features and the differences between them. We further dissect the molecular mechanisms of action of taxanes and the molecular basis underlying the onset of taxane resistance. We further delineate the possible modalities to overcome chemoresistance to taxanes, such as increasing drug solubility, delivery and pharmacokinetics, overcoming microtubule alterations or mitotic slippage, inhibiting drug efflux pumps or drug metabolism, targeting redox metabolism, immune response, and other cellular functions.

Friend or Foe: S100 Proteins in Cancer

S100 proteins are widely expressed small molecular EF-hand calcium-binding proteins of vertebrates, which are involved in numerous cellular processes, such as Ca²⁺ homeostasis, proliferation, apoptosis, differentiation, and inflammation. Although the complex network of S100 signalling is by far not fully deciphered, several S100 family members could be linked to a variety of diseases, such as inflammatory disorders, neurological diseases, and also cancer. The research of the past decades revealed that S100 proteins play a crucial role in the development and progression of many cancer types, such as breast cancer, lung cancer, and melanoma. Hence, S100 family members have also been shown to be promising diagnostic markers and possible novel targets for therapy. However, the current knowledge of S100 proteins is limited and more attention to this unique group of proteins is needed. Therefore, this review article summarises S100 proteins and their relation in different cancer types, while also providing an overview of novel therapeutic strategies for targeting S100 proteins for cancer treatment.

Mechanistic Involvement of Long Non-Coding RNAs in Oncotherapeutics Resistance in Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is one of the most lethal forms of breast cancer (BC), with a significant disease burden worldwide. Chemoresistance and lack of targeted therapeutics are major hindrances to effective treatments in the clinic and are crucial causes of a worse prognosis and high rate of relapse/recurrence in patients diagnosed with TNBC. In the last decade, long non-coding RNAs (lncRNAs) have been found to perform a pivotal role in most cellular functions. The aberrant functional expression of lncRNAs plays an ever-increasing role in the progression of diverse malignancies, including TNBC. Therefore, lncRNAs have been recently studied as predictors and modifiers of chemoresistance. Our review discusses the potential involvement of lncRNAs in drug-resistant mechanisms commonly found in TNBC and highlights various therapeutic strategies to target lncRNAs in this malignancy.

A gene signature for immune subtyping of desert, excluded, and inflamed ovarian tumors

PROBLEM

The current tumor immunology paradigm emphasizes the role of the immune tumor microenvironment and distinguishes several histologically and transcriptionally different immune tumor subtypes. However, the experimental validation of such classification is so far limited to selected cancer types. Here, we aimed to explore the existence of inflamed, excluded, and desert immune subtypes in ovarian cancer, as well as investigate their association with the disease outcome.

METHOD OF STUDY

We used the publicly available ovarian cancer dataset from The Cancer Genome Atlas for developing subtype assignment algorithm, which was next verified in a cohort of 32 real-world patients of a known tumor subtype.

RESULTS

Using clinical and gene expression data of 489 ovarian cancer patients in the publicly available dataset, we identified three transcriptionally distinct clusters, representing inflamed, excluded, and desert subtypes. We developed a two-step subtyping algorithm with COL5A2 serving as a marker for separating excluded tumors, and CD2, TAP1, and ICOS for distinguishing between inflamed and desert tumors. The accuracy of gene expression-based subtyping algorithm in a real-world cohort was 75%. Additionally, we confirmed that patients bearing inflamed tumors are more likely to survive longer.

CONCLUSION

Our results highlight the presence of transcriptionally and histologically distinct immune subtypes among ovarian tumors and emphasize the potential benefit of immune subtyping as a clinical tool for treatment tailoring.

Sparse relative risk regression models

Clinical studies where patients are routinely screened for many genomic features are becoming more routine. In principle, this holds the promise of being able to find genomic signatures for a particular disease. In particular, cancer survival is thought to be closely linked to the genomic constitution of the tumor. Discovering such signatures will be useful in the diagnosis of the patient, may be used for treatment decisions and, perhaps, even the development of new treatments. However, genomic data are typically noisy and high-dimensional, not rarely outstripping the number of patients included in the study. Regularized survival models have been proposed to deal with such scenarios. These methods typically induce sparsity by means of a coincidental match of the geometry of the convex likelihood and a (near) non-convex regularizer. The disadvantages of such methods are that they are typically non-invariant to scale changes of the covariates, they struggle with highly correlated covariates, and they have a practical problem of determining the amount of regularization. In this article, we propose an extension of the differential geometric least angle regression method for sparse inference in relative risk regression models. A software implementation of our method is available on github (https://github.com/LuigiAugugliaro/dgcox).

Multiple functions of S100A10, an important cancer promoter

The S100 group of calcium binding proteins is composed of 21 members that exhibit tissue/cell specific expressions. These S100 proteins bind a diverse range of targets and regulate multiple cellular processes, including proliferation, migration and differentiation. S100A10, also known as p11, binds mainly to annexin A2 and mediates the conversion of plasminogen to an active protease, plasmin. Higher S100A10 expression has been reported to link to worse outcome and/or chemoresistance in a number of cancer types in lung, breast, ovary, pancreas, gall bladder and colorectum and leukemia although some discrepancy was reported. In this review, we focused on the roles of the S100A10 in cancer. We summarized its biological functions, role in cancer progression, prognostic value and targeting of S100A10 for cancer therapy.

Novel Model for Comprehensive Assessment of Robust Prognostic Gene Signature in Ovarian Cancer Across Different Independent Datasets

Different analytical methods or models can often find completely different prognostic biomarkers for the same cancer. In the study of prognostic molecular biomarkers of ovarian cancer (OvCa), different studies have reported a variety of prognostic gene signatures. In the current study, based on geometric concepts, the linearity-clustering phase diagram with integrated P-value (LCP) method was used to comprehensively consider three indicators that are commonly employed to estimate the quality of a prognostic gene signature model. The three indicators, namely, concordance index, area under the curve, and level of the hazard ratio were determined via calculation of the prognostic index of various gene signatures from different datasets. As evaluation objects, we selected 13 gene signature models (Cox regression model) and 16 OvCa genomic datasets (including gene expression information and follow-up data) from published studies. The results of LCP showed that three models were universal and better than other models. In addition, combining the three models into one model showed the best performance in all datasets by LCP calculation. The combination gene signature model provides a more reliable model and could be validated in various datasets of OvCa. Thus, our method and findings can provide more accurate prognostic biomarkers and effective reference for the precise clinical treatment of OvCa.

Diagnosis of epithelial ovarian cancer using a combined protein biomarker panel

BACKGROUND

An early detection tool for EOC was constructed from analysis of biomarker expression data from serum collected during the UKCTOCS.

METHODS

This study included 49 EOC cases (19 Type I and 30 Type II) and 31 controls, representing 482 serial samples spanning seven years pre-diagnosis. A logit model was trained by analysis of dysregulation of expression data of four putative biomarkers, (CA125, phosphatidylcholine-sterol acyltransferase, vitamin K-dependent protein Z and C-reactive protein); by scoring the specificity associated with dysregulation from the baseline expression for each individual.

RESULTS

The model is discriminatory, passes k-fold and leave-one-out cross-validations and was further validated in a Type I EOC set. Samples were analysed as a simulated annual screening programme, the algorithm diagnosed cases with >30% PPV 1-2 years pre-diagnosis. For Type II cases (~80% were HGS) the algorithm classified 64% at 1 year and 28% at 2 years tDx as severe.

CONCLUSIONS

The panel has the potential to diagnose EOC one-two years earlier than current diagnosis. This analysis provides a tangible worked example demonstrating the potential for development as a screening tool and scrutiny of its properties. Limits on interpretation imposed by the number of samples available are discussed.

Drug resistance and combating drug resistance in cancer

Cancer is the second leading cause of death in the US. Current major treatments for cancer management include surgery, cytotoxic chemotherapy, targeted therapy, radiation therapy, endocrine therapy and immunotherapy. Despite the endeavors and achievements made in treating cancers during the past decades, resistance to classical chemotherapeutic agents and/or novel targeted drugs continues to be a major problem in cancer therapies. Drug resistance, either existing before treatment (intrinsic) or generated after therapy (acquired), is responsible for most relapses of cancer, one of the major causes of death of the disease. Heterogeneity among patients and tumors, and the versatility of cancer to circumvent therapies make drug resistance more challenging to deal with. Better understanding the mechanisms of drug resistance is required to provide guidance to future cancer treatment and achieve better outcomes. In this review, intrinsic and acquired resistance will be discussed. In addition, new discoveries in mechanisms of drug resistance will be reviewed. Particularly, we will highlight roles of ATP in drug resistance by discussing recent findings of exceptionally high levels of intratumoral extracellular ATP as well as intracellular ATP internalized from extracellular environment. The complexity of drug resistance development suggests that combinational and personalized therapies, which should take ATP into consideration, might provide better strategies and improved efficacy for fighting drug resistance in cancer.

TNF-α Modulates P-Glycoprotein Expression and Contributes to Cellular Proliferation via Extracellular Vesicles

P-glycoprotein (Pgp/ABCB1) overexpression is associated with multidrug resistance (MDR) phenotype and, consequently, failure in cancer chemotherapy. However, molecules involved in cell death deregulation may also support MDR. Tumor necrosis factor-alpha (TNF-α) is an important cytokine that may trigger either death or tumor growth. Here, we examined the role of cancer cells in self-maintenance and promotion of cellular malignancy through the transport of Pgp and TNF-α molecules by extracellular vesicles (membrane microparticles (MP)). By using a classical MDR model in vitro, we identified a positive correlation between endogenous TNF-α and Pgp, which possibly favored a non-cytotoxic effect of recombinant TNF-α (rTNF-α). We also found a positive feedback involving rTNF-α incubation and TNF-α regulation. On the other hand, rTNF-α induced a reduction in Pgp expression levels and contributed to a reduced Pgp efflux function. Our results also showed that parental and MDR cells spontaneously released MP containing endogenous TNF-α and Pgp. However, these MP were unable to transfer their content to non-cancer recipient cells. Nevertheless, MP released from parental and MDR cells elevated the proliferation index of non-tumor cells. Collectively, our results suggest that Pgp and endogenous TNF-α positively regulate cancer cell malignancy and contribute to changes in normal cell behavior through MP.

S100A10 and Cancer Hallmarks: Structure, Functions, and its Emerging Role in Ovarian Cancer

S100A10, which is also known as p11, is located in the plasma membrane and forms a heterotetramer with annexin A2. The heterotetramer, comprising of two subunits of annexin A2 and S100A10, activates the plasminogen activation pathway, which is involved in cellular repair of normal tissues. Increased expression of annexin A2 and S100A10 in cancer cells leads to increased levels of plasmin—which promotes the degradation of the extracellular matrix—increased angiogenesis, and the invasion of the surrounding organs. Although many studies have investigated the functional role of annexin A2 in cancer cells, including ovarian cancer, S100A10 has been less studied. We recently demonstrated that high stromal annexin A2 and high cytoplasmic S100A10 expression is associated with a 3.4-fold increased risk of progression and 7.9-fold risk of death in ovarian cancer patients. Other studies have linked S100A10 with multidrug resistance in ovarian cancer; however, no functional studies to date have been performed in ovarian cancer cells. This article reviews the current understanding of S100A10 function in cancer with a particular focus on ovarian cancer.

Platinum sensitivity of ovarian cancer cells does not influence their ability to induce M2-type macrophage polarization

PROBLEM

Development of platinum resistance in ovarian cancer is mediated by both cancer cells and tumor microenvironment. Activation of epithelial-mesenchymal transition program in cancer cells may lead to enrichment for resistant clones. These processes can be affected by tumor-associated macrophages, a highly plastic population of cells that participate in tumor progression and response to treatment by shaping the microenvironment. We aimed to study how platinum resistance influences the crosstalk between macrophages and ovarian cancer cells.

METHOD OF STUDY

Using cisplatin-sensitive ovarian cancer cell line A2780, we developed and characterized cisplatin-resistant A2780Cis and cisplatin and doxorubicin co-resistant A2780Dox cell lines. Next, we set up an indirect coculture system with THP-1 cell line-derived M0-type-, M1-type- and M2-type-like polarized macrophages. We monitored the expression of genes associated with cellular stemness, multidrug resistance, and epithelial-mesenchymal transition in cancer cells, and expression profile of M1/M2 markers in macrophages.

RESULTS

Development of drug resistance in ovarian cancer cell lines was accompanied by increased migration, clonogenicity, and upregulated expression of transcription factors, associated with cellular stemness and epithelial-mesenchymal transition. Upon coculture, we noted that the most relevant changes in gene expression profile occurred in A2780 cells. Moreover, M0- and M1-type macrophages, but not M2-type macrophages, showed significant transcriptional alterations.

CONCLUSION

Our results provide the evidence for bidirectional interplay between cancer cells and macrophages. Independent of platinum resistance status, ovarian cancer cells polarize macrophages toward M2-like type, whereas macrophages induce epithelial-mesenchymal transition and stemness-related gene expression profile in cisplatin-sensitive, but not cisplatin-resistant cancer cells.

Multidrug resistant lncRNA profile in chemotherapeutic sensitive and resistant ovarian cancer cells

Most ovarian cancer patients are chemosensitive initially, but finally relapse with acquired chemoresistance. Multidrug-resistance is the extremely terrible situation. The mechanism for the acquired chemoresistance of ovarian cancer patients is still not clear. LncRNAs have been recognized as the important regulator of a variety of biological processes, including the multidrug-resistant process. Here, we carried out the lncRNA sequencing of the ovarian cancer cell line A2780 and the paxitaxel resistant cell line A2780/PTX which is also cross resistant to the cisplatin and epirubicin. Through integrating the published data with the cisplatin resistant lncRNAs in ovarian cancer cell line or ovarian cancer patients, 5 up-regulated and 21 down-regulated lncRNAs are considered as the multidrug-resistant lncRNAs. By real-time PCR analysis, we confirmed the 5 up-regulated and 4 down-regulated multidrug resistant lncRNAs were similarly changed in both the multidrug resistant ovarian cancer cell lines and the multidrug resistant colon cancer cell lines. Furthermore, we conducted the lncRNA-mRNA co-expression network to predict the potential multidrug resistant lncRNAs' targets. Interestingly, the multidrug resistant genes ABCB1, ABCB4, ABCC3, and ABCG2 are all co-expressed with lncRNA CTD-2589M5.4. Our results provide the valuable information for the understanding of the lncRNA function in the multidrug resistant process.

Tumor-Host Cell Interactions in Ovarian Cancer: Pathways to Therapy Failure

Although most ovarian cancer patients are highly responsive to chemotherapy, they frequently present with recurrent metastatic lesions that result in poor overall survival, a situation that has not changed in the last 20 years. This review discusses new insights into the regulation of ovarian cancer chemoresistance with a focus on the emerging role of immune and other host cells. Here, we summarize the complex molecular pathways that regulate the interaction between tumor and host cells, discuss the limitations of current in vitro and in vivo models for translational studies, and present perspectives for the development of innovative therapies.

Novel risk models for early detection and screening of ovarian cancer

PURPOSE

Ovarian cancer (OC) is the most lethal gynaecological cancer. Early detection is required to improve patient survival. Risk estimation models were constructed for Type I (Model I) and Type II (Model II) OC from analysis of Protein Z, Fibronectin, C-reactive protein and CA125 levels in prospectively collected samples from the United Kingdom Collaborative Trial of Ovarian Cancer Screening (UKCTOCS).

RESULTS

Model I identifies cancers earlier than CA125 alone, with a potential lead time of 3-4 years. Model II detects a number of high grade serous cancers at an earlier stage (Stage I/II) than CA125 alone, with a potential lead time of 2-3 years and assigns high risk to patients that the ROCA Algorithm classified as normal.

MATERIALS AND METHODS

This nested case control study included 418 individual serum samples serially collected from 49 OC cases and 31 controls up to six years pre-diagnosis. Discriminatory logit models were built combining the ELISA results for candidate proteins with CA125 levels.

CONCLUSIONS

These models have encouraging sensitivities for detecting pre-clinical ovarian cancer, demonstrating improved sensitivity compared to CA125 alone. In addition we demonstrate how the models improve on ROCA for some cases and outline their potential future use as clinical tools.

AID/APOBEC-network reconstruction identifies pathways associated with survival in ovarian cancer

BACKGROUND

Building up of pathway-/disease-relevant signatures provides a persuasive tool for understanding the functional relevance of gene alterations and gene network associations in multifactorial human diseases. Ovarian cancer is a highly complex heterogeneous malignancy in respect of tumor anatomy, tumor microenvironment including pro-/antitumor immunity and inflammation; still, it is generally treated as single disease. Thus, further approaches to investigate novel aspects of ovarian cancer pathogenesis aiming to provide a personalized strategy to clinical decision making are of high priority. Herein we assessed the contribution of the AID/APOBEC family and their associated genes given the remarkable ability of AID and APOBECs to edit DNA/RNA, and as such, providing tools for genetic and epigenetic alterations potentially leading to reprogramming of tumor cells, stroma and immune cells.

RESULTS

We structured the study by three consecutive analytical modules, which include the multigene-based expression profiling in a cohort of patients with primary serous ovarian cancer using a self-created AID/APOBEC-associated gene signature, building up of multivariable survival models with high predictive accuracy and nomination of top-ranked candidate/target genes according to their prognostic impact, and systems biology-based reconstruction of the AID/APOBEC-driven disease-relevant mechanisms using transcriptomics data from ovarian cancer samples. We demonstrated that inclusion of the AID/APOBEC signature-based variables significantly improves the clinicopathological variables-based survival prognostication allowing significant patient stratification. Furthermore, several of the profiling-derived variables such as ID3, PTPRC/CD45, AID, APOBEC3G, and ID2 exceed the prognostic impact of some clinicopathological variables. We next extended the signature-/modeling-based knowledge by extracting top genes co-regulated with target molecules in ovarian cancer tissues and dissected potential networks/pathways/regulators contributing to pathomechanisms. We thereby revealed that the AID/APOBEC-related network in ovarian cancer is particularly associated with remodeling/fibrotic pathways, altered immune response, and autoimmune disorders with inflammatory background.

CONCLUSIONS

The herein study is, to our knowledge, the first one linking expression of entire AID/APOBECs and interacting genes with clinical outcome with respect to survival of cancer patients. Overall, data propose a novel AID/APOBEC-derived survival model for patient risk assessment and reconstitute mapping to molecular pathways. The established study algorithm can be applied further for any biologically relevant signature and any type of diseased tissue.

Annexin A2 and S100A10 are independent predictors of serous ovarian cancer outcome

Annexin A2, a calcium phospholipid binding protein, has been shown to play an important role in ovarian cancer metastasis. This study examined whether annexin A2 and S100A10 can be used as prognostic markers in serous ovarian cancer. ANXA2 and S100A10 gene expressions were assessed in publicly available ovarian cancer data sets and annexin A2 and S100A10 protein expressions were assessed by immunohistochemistry in a uniform cohort of stage III serous ovarian cancers (n = 109). Kaplan-Meier and Cox regression analyses were performed to assess the relationship between annexin A2 or S100A10 messenger RNA (mRNA) and protein expressions with clinical outcome. High ANXA2 mRNA levels in stage III serous ovarian cancers were associated with reduced progression-free survival (PFS; P = 0.023) and overall survival (OS; P = 0.0038), whereas high S100A10 mRNA levels predicted reduced OS (P = 0.0019). Using The Cancer Genome Atlas data sets, ANXA2 but not S100A10 expression was associated with higher clinical stage (P = 0.005), whereas both ANXA2 and S100A10 expressions were associated with the mesenchymal molecular subtype (P < 0.0001). Kaplan-Meier and Cox regression analyses showed that high stromal annexin A2 immunostaining was significantly associated with reduced PFS (P = 0.013) and OS (P = 0.044). Moreover, high cytoplasmic S100A10 staining was significantly associated with reduced OS (P = 0.027). Multivariate Cox regression analysis showed stromal annexin A2 (P = 0.009) and cytoplasmic S100A10 (P = 0.016) levels to be independent predictors of OS. Patients with high stromal annexin A2 and high cytoplasmic S100A10 expressions had a 3.4-fold increased risk of progression (P = 0.02) and 7.9-fold risk of ovarian cancer death (P = 0.04). Our findings indicate that together annexin A2 and S100A10 expressions are powerful predictors of serous ovarian cancer outcome.

Recently identified drug resistance biomarkers in ovarian cancer

Ovarian cancer, consisting mainly of ovarian carcinoma, is the most lethal gynecologic malignancy. Improvements in outcome for patients with advanced-stage disease are limited by intrinsic and acquired chemoresistance and by tumor heterogeneity at different anatomic sites and along disease progression. Molecules and cellular pathways mediating chemoresistance appear to be different for the different histological types of ovarian carcinoma, with most recent research focusing on serous and clear cell carcinoma. This review discusses recent data implicating various biomarkers in chemoresistance in this cancer, with focus on studies in which clinical specimens have been central.

Toward a Better Understanding of the Complexity of Cancer Drug Resistance

Resistance to anticancer drugs is a complex process that results from alterations in drug targets; development of alternative pathways for growth activation; changes in cellular pharmacology, including increased drug efflux; regulatory changes that alter differentiation pathways or pathways for response to environmental adversity; and/or changes in the local physiology of the cancer, such as blood supply, tissue hydrodynamics, behavior of neighboring cells, and immune system response. All of these specific mechanisms are facilitated by the intrinsic hallmarks of cancer, such as tumor cell heterogeneity, redundancy of growth-promoting pathways, increased mutation rate and/or epigenetic alterations, and the dynamic variation of tumor behavior in time and space. Understanding the relative contribution of each of these factors is further complicated by the lack of adequate in vitro models that mimic clinical cancers. Several strategies to use current knowledge of drug resistance to improve treatment of cancer are suggested.

Risk prediction model for epithelial ovarian cancer using molecular markers and clinical characteristics

BACKGROUND

A high-quality risk prediction model is urgently needed for the clinical management of ovarian cancer. However most existing models are solely based on clinical parameters, and molecular classifications in recent reports are still being debated. This study aimed to establish a risk prediction model by using both clinicopathological and molecular factors (the synthetic model) for epithelial ovarian cancer.

METHODS

A retrospective cohort study was conducted in epithelial ovarian cancer patients (n = 161) treated with primary debulking surgery and adjuvant chemotherapy. The expression level of 15 selected molecular markers were measured using immunohistochemistry. A risk model was developed using COX regression analysis with overall survival as the primary outcome. A simplified scoring system for each prognostic factor was based on its coefficient. Independent validation (n = 40) was conducted to evaluate the performance of the model.

RESULTS

A total of 10 out of 15 molecular markers were significantly associated with clinical characteristics and overall survival. The synthetic model performed better than the clinicopathological risk model or the molecular risk model alone, as assessed by analysis of the receiver-operating characteristics curve area and the Youden index. The synthetic model included parity (>3), peritoneal metastasis, stage, tumor type, residual disease, and expression of human epidermal growth factor receptor 2 (HER2), epidermal growth factor receptor (EGFR), breast cancer 1 (BRCA1), murine sarcoma viral oncogene homolog B (BRAF) and Kirsten rat sarcoma viral oncogene homolog (KRAS).

CONCLUSIONS

Our synthetic risk model may more accurately predict survival of epithelial ovarian cancer patients than current models.

Expression profile of COL2A1 and the pseudogene SLC6A10P predicts tumor recurrence in high-grade serous ovarian cancer

Tumor recurrence, following initial response to adjuvant chemotherapy, is a major problem in women with high-grade serous ovarian cancer (HGSOC). Microarray analysis of primary tumors has identified genes that may be useful in risk stratification/overall survival, but are of limited value in predicting the >70% rate for tumor recurrence. In this study, we performed RNA-Seq analysis of primary and recurrent HGSOC to first identify unique differentially expressed genes. From this dataset, we selected 21 archetypical coding genes and one noncoding RNA, based on statistically significant differences in their expression profile between tumors, for validation by qPCR in a larger cohort of 110 ovarian tumors (71 primary and 39 recurrent) and for testing association of specific genes with time-to-recurrence (TTR). Kaplan-Meier tests revealed that high expression of collagen type II, alpha 1 (COL2A1) was associated with delayed TTR (HR = 0.47, 95% CI: 0.27-0.82, p = 0.008), whereas low expression of the pseudogene, solute carrier family 6 member 10 (SLC6A10P), was associated with longer TTR (HR = 0.53, 95% CI: 0.30-0.93, p = 0.027). Notably, TTR was significantly delayed for tumors that simultaneously highly expressed COL2A1 and lowly expressed SLC6A10P (HR = 0.21, 95% CI: 0.082-0.54, p = 0.0011), an estimated median of 95 months as compared to an estimated median of 16 months for subjects expressing other levels of COL2A1 and SLC6A10P. Thus, evaluating expression levels of COL2A1 and SLC6A10P at primary surgery could be beneficial for clinically managing recurrence of HGSOC.

Expression and clinical role of chemoresponse-associated genes in ovarian serous carcinoma

OBJECTIVE

To validate our earlier observation that 11 chemoresistance-associated mRNAs are molecular markers of poor overall survival in ovarian serous carcinoma.

METHODS

Ovarian serous carcinomas (n=112) and solid metastases (n=63; total=175) were analyzed for mRNA expression of APC, BAG3, EGFR, S100A10, ITGAE, MAPK3, TAP1, BNIP3, MMP9, FASLG and GPX3 using quantitative real-time PCR. mRNA expression was studied for association with clinicopathologic parameters and survival. Tumor heterogeneity was assessed in 20 cases with >1 specimen per patient. APC, BAG3, S100A10 and ERK1 protein expression by immunohistochemistry was analyzed in 58 specimens (38 primary carcinomas, 20 metastases).

RESULTS

BAG3 (p=0.013), TAP1 (p=0.014), BNIP3 (p<0.001) and MMP9 (p=0.036) were overexpressed in primary tumors, whereas S100A10 (p=0.027) and FASLG (p=0.006) were overexpressed in metastases. Analysis of patient-matched primary carcinomas and metastases showed overexpression of APC (p=0.022), MAPK3 (p=0.002) and BNIP3 (p=0.004) in the former. In primary carcinomas, higher APC (p=0.003) and MAPK3 (p=0.005) levels were related to less favorable chemoresponse. Higher S100A10 (p=0.029) and MAPK3 (p=0.041) levels were related to primary chemoresistance. Higher BAG3 (p=0.026) and APC (p=0.046) levels in primary carcinomas were significantly related to poor overall survival in univariate, though not in multivariate survival analysis. S100A10 protein expression was related to poor chemoresponse (p=0.002) and shorter overall (p=0.005) and progression-free (p<0.001) survival, the latter finding retained in multivariate analysis (p=0.035).

CONCLUSIONS

Our data provide evidence of heterogeneity in ovarian serous carcinoma and identify APC, MAPK3, BAG3 and S100A10 as potential biomarkers of poor chemotherapy response and/or poor outcome in this cancer.

Prediction of resistance to chemotherapy in ovarian cancer: a systematic review

Background

Patient response to chemotherapy for ovarian cancer is extremely heterogeneous and there are currently no tools to aid the prediction of sensitivity or resistance to chemotherapy and allow treatment stratification. Such a tool could greatly improve patient survival by identifying the most appropriate treatment on a patient-specific basis.

Methods

PubMed was searched for studies predicting response or resistance to chemotherapy using gene expression measurements of human tissue in ovarian cancer.

Results

42 studies were identified and both the data collection and modelling methods were compared. The majority of studies utilised fresh-frozen or formalin-fixed paraffin-embedded tissue. Modelling techniques varied, the most popular being Cox proportional hazards regression and hierarchical clustering which were used by 17 and 11 studies respectively. The gene signatures identified by the various studies were not consistent, with very few genes being identified by more than two studies. Patient cohorts were often noted to be heterogeneous with respect to chemotherapy treatment undergone by patients.

Conclusions

A clinically applicable gene signature capable of predicting patient response to chemotherapy has not yet been identified. Research into a predictive, as opposed to prognostic, model could be highly beneficial and aid the identification of the most suitable treatment for patients.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-015-1101-8) contains supplementary material, which is available to authorized users.

APOA1 mRNA expression in ovarian serous carcinoma effusions is a marker of longer survival

OBJECTIVES

We previously described the overexpression of APOA1 and GPX3 in ovarian/peritoneal serous carcinoma compared with breast carcinoma effusions using gene expression array analysis. The objective of the present study was to validate this finding and to analyze the association between these genes and clinicopathologic parameters, including survival, in advanced-stage ovarian serous carcinoma.

METHODS

APOA1 and GPX3 mRNA expression using quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) was analyzed in 121 effusions (101 ovarian, 20 breast carcinomas) and 85 solid ovarian carcinoma specimens (43 primary carcinomas, 42 metastases).

RESULTS

APOA1 and GPX3 transcript levels were significantly higher in ovarian carcinoma at all anatomic sites compared with breast carcinoma effusions (P < .001). GPX3 mRNA levels were significantly higher in primary carcinomas and solid metastases from patients who received neoadjuvant chemotherapy compared with chemo-naïve tumors (P = .016). APOA1 and GPX3 mRNA levels in the entire effusion series were unrelated to clinicopathologic parameters. However, higher APOA1 mRNA levels in primary diagnosis pre-chemotherapy effusions were significantly related to better overall survival (P = .045), a finding that retained its significance in Cox multivariate analysis (P = .016).

CONCLUSIONS

APOA1 and GPX3 mRNA levels on qRT-PCR effectively differentiate ovarian from breast carcinoma. APOA1 may be a novel prognostic marker in metastatic serous carcinoma.

Inhibition of glutathione peroxidase mediates the collateral sensitivity of multidrug-resistant cells to tiopronin

Multidrug resistance (MDR) is a major obstacle to the successful chemotherapy of cancer. MDR is often the result of overexpression of ATP-binding cassette transporters following chemotherapy. A common ATP-binding cassette transporter that is overexpressed in MDR cancer cells is P-glycoprotein, which actively effluxes drugs against a concentration gradient, producing an MDR phenotype. Collateral sensitivity (CS), a phenomenon of drug hypersensitivity, is defined as the ability of certain compounds to selectively target MDR cells, but not the drug-sensitive parent cells from which they were derived. The drug tiopronin has been previously shown to elicit CS. However, unlike other CS agents, the mechanism of action was not dependent on the expression of P-glycoprotein in MDR cells. We have determined that the CS activity of tiopronin is mediated by the generation of reactive oxygen species (ROS) and that CS can be reversed by a variety of ROS-scavenging compounds. Specifically, selective toxicity of tiopronin toward MDR cells is achieved by inhibition of glutathione peroxidase (GPx), and the mode of inhibition of GPx1 by tiopronin is shown in this report. Why MDR cells are particularly sensitive to ROS is discussed, as is the difficulty in exploiting this hypersensitivity to tiopronin in the clinic.

Risk prediction for late-stage ovarian cancer by meta-analysis of 1525 patient samples

BACKGROUND

Ovarian cancer causes more than 15000 deaths per year in the United States. The survival of patients is quite heterogeneous, and accurate prognostic tools would help with the clinical management of these patients.

METHODS

We developed and validated two gene expression signatures, the first for predicting survival in advanced-stage, serous ovarian cancer and the second for predicting debulking status. We integrated 13 publicly available datasets totaling 1525 subjects. We trained prediction models using a meta-analysis variation on the compound covariable method, tested models by a "leave-one-dataset-out" procedure, and validated models in additional independent datasets. Selected genes from the debulking signature were validated by immunohistochemistry and quantitative reverse-transcription polymerase chain reaction (qRT-PCR) in two further independent cohorts of 179 and 78 patients, respectively. All statistical tests were two-sided.

RESULTS

The survival signature stratified patients into high- and low-risk groups (hazard ratio = 2.19; 95% confidence interval [CI] = 1.84 to 2.61) statistically significantly better than the TCGA signature (P = .04). POSTN, CXCL14, FAP, NUAK1, PTCH1, and TGFBR2 were validated by qRT-PCR (P < .05) and POSTN, CXCL14, and phosphorylated Smad2/3 were validated by immunohistochemistry (P < .001) as independent predictors of debulking status. The sum of immunohistochemistry intensities for these three proteins provided a tool that classified 92.8% of samples correctly in high- and low-risk groups for suboptimal debulking (area under the curve = 0.89; 95% CI = 0.84 to 0.93).

CONCLUSIONS

Our survival signature provides the most accurate and validated prognostic model for early- and advanced-stage high-grade, serous ovarian cancer. The debulking signature accurately predicts the outcome of cytoreductive surgery, potentially allowing for stratification of patients for primary vs secondary cytoreduction.

Risk prediction for late-stage ovarian cancer by meta-analysis of 1525 patient samples

BACKGROUND

Ovarian cancer causes more than 15000 deaths per year in the United States. The survival of patients is quite heterogeneous, and accurate prognostic tools would help with the clinical management of these patients.

METHODS

We developed and validated two gene expression signatures, the first for predicting survival in advanced-stage, serous ovarian cancer and the second for predicting debulking status. We integrated 13 publicly available datasets totaling 1525 subjects. We trained prediction models using a meta-analysis variation on the compound covariable method, tested models by a "leave-one-dataset-out" procedure, and validated models in additional independent datasets. Selected genes from the debulking signature were validated by immunohistochemistry and quantitative reverse-transcription polymerase chain reaction (qRT-PCR) in two further independent cohorts of 179 and 78 patients, respectively. All statistical tests were two-sided.

RESULTS

The survival signature stratified patients into high- and low-risk groups (hazard ratio = 2.19; 95% confidence interval [CI] = 1.84 to 2.61) statistically significantly better than the TCGA signature (P = .04). POSTN, CXCL14, FAP, NUAK1, PTCH1, and TGFBR2 were validated by qRT-PCR (P < .05) and POSTN, CXCL14, and phosphorylated Smad2/3 were validated by immunohistochemistry (P < .001) as independent predictors of debulking status. The sum of immunohistochemistry intensities for these three proteins provided a tool that classified 92.8% of samples correctly in high- and low-risk groups for suboptimal debulking (area under the curve = 0.89; 95% CI = 0.84 to 0.93).

CONCLUSIONS

Our survival signature provides the most accurate and validated prognostic model for early- and advanced-stage high-grade, serous ovarian cancer. The debulking signature accurately predicts the outcome of cytoreductive surgery, potentially allowing for stratification of patients for primary vs secondary cytoreduction.

Identification and analysis of CD133(+) melanoma stem-like cells conferring resistance to taxol: An insight into the mechanisms of their resistance and response

The presence and the involvement of cancer stem-like cells (CSCs) in tumor initiation and progression, and chemo-resistance are documented. Herein, we functionally analyzed melanoma stem-like cells (MSC)/CD133(+) cells on their resistance and response to taxol-induced apoptosis. Besides being taxol resistant, the CD133(+) cells demonstrated a growth advantage over the CD133(-) subpopulation. Taxol induced apoptosis on CD133(-) cells, but not on CD133(+) cells. In the CD133(-) subpopulation, the exposure to taxol induced the activation of apoptosis signal-regulating kinase1 (ASK1)/c-jun-N-terminal kinase (JNK), p38, extracellular signal regulated kinase (ERK) pathways and Bax expression, while in CD133(+) cells taxol was able only to enhance the activity of the ERK pathway. In CD133(+) cells, the direct gene transfer of Bax overcame the acquired resistance to taxol. Taken together, our data provide an insight into the mechanistic cascade of melanoma resistance to taxol and suggest Bax gene transfer as a complementary approach to chemotherapy.

curatedOvarianData: clinically annotated data for the ovarian cancer transcriptome

This article introduces a manually curated data collection for gene expression meta-analysis of patients with ovarian cancer and software for reproducible preparation of similar databases. This resource provides uniformly prepared microarray data for 2970 patients from 23 studies with curated and documented clinical metadata. It allows users to efficiently identify studies and patient subgroups of interest for analysis and to perform meta-analysis immediately without the challenges posed by harmonizing heterogeneous microarray technologies, study designs, expression data processing methods and clinical data formats. We confirm that the recently proposed biomarker CXCL12 is associated with patient survival, independently of stage and optimal surgical debulking, which was possible only through meta-analysis owing to insufficient sample sizes of the individual studies. The database is implemented as the curatedOvarianData Bioconductor package for the R statistical computing language, providing a comprehensive and flexible resource for clinically oriented investigation of the ovarian cancer transcriptome. The package and pipeline for producing it are available from http://bcb.dfci.harvard.edu/ovariancancer.

Database URL:http://bcb.dfci.harvard.edu/ovariancancer

Apoptosis of osteosarcoma cultures by the combination of the cyclin-dependent kinase inhibitor SCH727965 and a heat shock protein 90 inhibitor

Osteosarcoma (OS) is an aggressive bone cancer typically observed in adolescents and young adults. Metastatic relapse accounts primarily for treatment failure, and obstacles to improving cure rates include a lack of efficacious agents. Our studies show apoptosis of OS cells prepared from localized and metastatic tumors by a novel drug combination: SCH727965 (SCH), a cyclin-dependent kinase inhibitor, and NVP-AUY922 (AUY) or other heat shock protein 90 inhibitor. SCH and AUY induced apoptosis when added simultaneously to cells and when AUY was added to and removed from cells before SCH addition. Sequential treatment was most effective when cells received AUY for ~12 h and when SCH was presented to cells immediately after AUY removal. The apoptotic protein Bax accumulated in mitochondria of cotreated cells but was primarily cytosolic in cells receiving either agent alone. Additional data show that SCH and AUY cooperatively induce the apoptosis of other sarcoma cell types but not of normal osteoblasts or fibroblasts, and that SCH and AUY individually inhibit cell cycle progression throughout the cell cycle. We suggest that the combination of SCH and AUY may be an effective new strategy for treatment of OS.

The clinical relevance of cancer cell lines

Although advances in genomics during the last decade have opened new avenues for translational research and allowed the direct evaluation of clinical samples, there is still a need for reliable preclinical models to test therapeutic strategies. Human cancer-derived cell lines are the most widely used models to study the biology of cancer and to test hypotheses to improve the efficacy of cancer treatment. Since the development of the first cancer cell line, the clinical relevance of these models has been continuously questioned. Based upon recent studies that have fueled the debate, we review the major events in the development of the in vitro models and the emergence of new technologies that have revealed important issues and limitations concerning human cancer cell lines as models. All cancer cell lines do not have equal value as tumor models. Some have been successful, whereas others have failed. However, the success stories should not obscure the growing body of data that motivates us to develop new in vitro preclinical models that would substantially increase the success rate of new in vitro-assessed cancer treatments.

Advances in the molecular detection of ABC transporters involved in multidrug resistance in cancer

ATP-Binding Cassette (ABC) transporters are important mediators of multidrug resistance (MDR) in patients with cancer. Although their role in MDR has been extensively studied in vitro, their value in predicting response to chemotherapy has yet to be fully determined. Establishing a molecular diagnostic assay dedicated to the quantitation of ABC transporter genes is therefore critical to investigate their involvement in clinical MDR. In this article, we provide an overview of the methodologies that have been applied to analyze the mRNA expression levels of ABC transporters, by describing the technology, its pros and cons, and the experimental protocols that have been followed. We also discuss recent studies performed in our laboratory that assess the ability of the currently available high-throughput gene expression profiling platforms to discriminate between highly homologous genes. This work led to the conclusion that high-throughput TaqMan-based qRT-PCR platforms provide standardized clinical assays for the molecular detection of ABC transporters and other families of highly homologous MDR-linked genes encoding, for example, the uptake transporters (solute carriers-SLCs) and the phase I and II metabolism enzymes.