Personalized medicine in oncology: where have we come from and where are we going?

Pyrrole-based EGFR inhibitors for the treatment of NCSLC: Binding modes and SARs investigations

The treatment of advanced non-small cell lung cancer (NSCLC) has made substantial progress due to the rapid development of small molecule targeted therapy, with dramatically prolonged survival. As an effective drug for the treatment of NSCLC, epidermal growth factor receptor (EGFR) inhibitors are currently experiencing issues like severe adverse events and drug resistance. It is urgent to develop novel types of EGFR inhibitors to overcome the abovementioned limitations. Pyrrole always works well as a probe for the creation of novel medication candidates for hard-to-treat conditions like lung cancer. Although the design, synthesis, and biological assays of pyrrole derivatives have been reported, their inhibitory actions against the receptor tyrosine kinase (RTK) EGFR have not been in-depthly studied. This review highlights the small molecule EGFR inhibitors containing pyrrole heterocyclic pharmacophores in recent years, and the research on their mechanism, biological activity, and structure-activity relationship (SAR).

UPLC-MS/MS-Based Analysis of Trastuzumab in Plasma Samples: Application in Breast Cancer Patients Sample Monitoring

Trastuzumab is a target-based recombinant humanized IgG1 monoclonal antibody (mAbs), extensively employed for treatment of metastatic breast cancer with human epidermal growth receptor 2 (HER2) overexpression. Studies around the world have reported that mAbs have substantial inter-patient unpredictable absorption, distribution, metabolism, and excretion (ADME-pharmacokinetics) because of multiple elements manipulating the concentration of mAbs in plasma. Herein, we have established a bioanalytical technique using UPLC-MS/MS with an easy sample workup method and in-solution digestion protocol to assay the trastuzumab plasma samples from breast cancer patients in clinical studies. Surrogated proteolytic peptides were used for accurate quantification of trastuzumab (CanMab) with a trastuzumab signature peptide with [13C6, 15N4]-arginine and [13C6, 15N2]-lysine stable isotope-labeled (SIL) peptide. Experiments to validate the method were accurately carried out according to the guidelines mentioned in the bioanalytical method validation protocol. The evaluation established excellent linearity over a wide range of 5–500 µg/mL. The experimental procedure was efficaciously performed in a pilot study of five breast cancer patients and residual concentrations of drugs from responding and non-responding subjects were compared. The receiver operating characteristic (ROC) examination displayed that 52.25 µg/mL was the Cmin threshold predictive response with a satisfactory sensitivity of 88.58% and specificity of 79.25%.

A minimal model of T cell avidity may identify subtherapeutic vaccine schedules

T cells protect the body from cancer by recognising tumour-associated antigens. Recognising these antigens depends on multiple factors, one of which is T cell avidity, i.e., the total interaction strength between a T cell and a cancer cell. While both high- and low-avidity T cells can kill cancer cells, durable anti-cancer immune responses require the selection of high-avidity T cells. Previous experimentation with anti-cancer vaccines, however, has shown that most vaccines elicit low-avidity T cells. Optimising vaccine schedules may remedy this by preferentially selecting high-avidity T cells. Here, we use mathematical modelling to develop a simple, phenomenological model of avidity selection that may identify vaccine schedules that disproportionately favour low-avidity T cells. We calibrate our model to our prior, more complex model, and then validate it against several experimental data sets. We find that the sensitivity of the model's parameters change with vaccine dosage, which allows us to use a patient's data and clinical history to screen for suitable vaccine strategies.

Cetuximab Exhibits Sex Differences in Lymphatic Exposure after Intravenous Administration in Rats in the Absence of Differences in Plasma Exposure

PURPOSE

The aim of this work was to identify whether biochemical and physiological sources of mAb pharmacokinetic sex-effects could be identified in the rat model where target-mediated disposition is avoided.

METHODS

Plasma and lymphatic pharmacokinetics of the humanised anti-EGFR antibody cetuximab, along with potential physiological and biochemical drivers of pharmacokinetic sex differences, were examined in male and female rats. Cetuximab was used as a model mAb since plasma clearance is slower in female patients.

RESULTS

When plasma concentrations were normalised to dose, female rats displayed slower plasma clearance than males, but no significant differences were observed in liver and spleen biodistribution. Sex differences in apparent plasma clearance, however, were abolished after normalisation to body weight, surface area or fat-free mass. Significant sex differences were observed in plasma testosterone, endogenous IgG and fat free mass, but did not correlate with apparent clearance. Females did, however, show two-fold higher lymphatic exposure compared to males.

CONCLUSIONS

These data suggested that mAbs more efficiently access lymph in females, but this does not affect plasma pharmacokinetics or biodistribution. Further, the data suggest that sex differences observed in humans could be a function of antigen density.

Computational algorithms for in silico profiling of activating mutations in cancer

Methods to catalog and computationally assess the mutational landscape of proteins in human cancers are desirable. One approach is to adapt evolutionary or data-driven methods developed for predicting whether a single-nucleotide polymorphism (SNP) is deleterious to protein structure and function. In cases where understanding the mechanism of protein activation and regulation is desired, an alternative approach is to employ structure-based computational approaches to predict the effects of point mutations. Through a case study of mutations in kinase domains of three proteins, namely, the anaplastic lymphoma kinase (ALK) in pediatric neuroblastoma patients, serine/threonine-protein kinase B-Raf (BRAF) in melanoma patients, and erythroblastic oncogene B 2 (ErbB2 or HER2) in breast cancer patients, we compare the two approaches above. We find that the structure-based method is most appropriate for developing a binary classification of several different mutations, especially infrequently occurring ones, concerning the activation status of the given target protein. This approach is especially useful if the effects of mutations on the interactions of inhibitors with the target proteins are being sought. However, many patients will present with mutations spread across different target proteins, making structure-based models computationally demanding to implement and execute. In this situation, data-driven methods-including those based on machine learning techniques and evolutionary methods-are most appropriate for recognizing and illuminate mutational patterns. We show, however, that, in the present status of the field, the two methods have very different accuracies and confidence values, and hence, the optimal choice of their deployment is context-dependent.

Developing a Minimally Structured Mathematical Model of Cancer Treatment with Oncolytic Viruses and Dendritic Cell Injections

Mathematical models of biological systems must strike a balance between being sufficiently complex to capture important biological features, while being simple enough that they remain tractable through analysis or simulation. In this work, we rigorously explore how to balance these competing interests when modeling murine melanoma treatment with oncolytic viruses and dendritic cell injections. Previously, we developed a system of six ordinary differential equations containing fourteen parameters that well describes experimental data on the efficacy of these treatments. Here, we explore whether this previously developed model is the minimal model needed to accurately describe the data. Using a variety of techniques, including sensitivity analyses and a parameter sloppiness analysis, we find that our model can be reduced by one variable and three parameters and still give excellent fits to the data. We also argue that our model is not too simple to capture the dynamics of the data, and that the original and minimal models make similar predictions about the efficacy and robustness of protocols not considered in experiments. Reducing the model to its minimal form allows us to increase the tractability of the system in the face of parametric uncertainty.

Heat shock protein 27 influences the anti-cancer effect of curcumin in colon cancer cells through ROS production and autophagy activation

The problem of therapeutic resistance and chemotherapeutic efficacy is tricky and critical in the management of colorectal cancer (CRC). Curcumin is a promising anti-cancer agent. Heat shock protein 27 (HSP27) is correlated with CRC progression and is said to affect CRC response to different therapies. However, the role of HSP27 on the therapeutic efficacy of curcumin remains unknown. HSP27 was silenced using small hairpin RNA (shRNA) technique. The cytotoxic and apoptotic effects of curcumin were assessed by sulforhodamine B (SRB) colorimetric assay, flow cytometric cell cycle analysis, and annexin V/propidium iodide (PI) double-labeling assays. Total reactive oxygen species (ROS)/superoxide and autophagy detection were performed, and the levels of apoptosis-related proteins were examined by Western blotting. It was found that the silencing of HSP27 (HSP27-KD) resulted in increased treatment resistance to curcumin in CRC cells. In addition, cell cycle analysis showed that the curcumin treatment caused cell cycle arrest at the G2/M phase in the control group, and apoptosis was reduced in the HSP27-KD group. Curcumin treatment also resulted in a decrease in anti-apoptotic proteins, p-Akt, Akt, Bcl-2 and p-Bad, and increase in pro-apoptotic proteins Bad and c-PARP levels in the control cells but not in the HSP27-KD cells. This was also followed by low reactive oxygen/nitrogen species (ROS/RNS), superoxide and autophagy induction levels in the HSP27-KD cells as compared to the control cells. Therefore, as silencing of HSP27 increases curcumin resistance by reducing apoptosis and reactive oxidative stress production, HSP27 is a potential selective target for curcumin treatment in CRC.

Host-dependent variables: The missing link to personalized medicine

Individualized medicine has the potential to tailor anticancer therapy with the best response and highest safety margin to provide better patient care. However, modern targeted therapies are still being tested through clinical trials comparing preselected patient cohorts and assessed upon behaviour of group averages. Clinically manifesting malignant disease requires identification of host- and tumour-dependent variables such as biological characteristics of the tumour and its microenvironment including immune response features, and overall capacity of the host to receive, tolerate and efficiently utilize treatment. Contemporary medical oncology including clinical trial design need to refocus from assessing group averages to individuality taking into consideration time dependent host-associated characteristics and reinventing outliers to be appreciated as naturally occurring variables collectively determining the ultimate outcome of malignant disease.

Evaluating optimal therapy robustness by virtual expansion of a sample population, with a case study in cancer immunotherapy

Cancer is a highly heterogeneous disease, exhibiting spatial and temporal variations that pose challenges for designing robust therapies. Here, we propose the VEPART (Virtual Expansion of Populations for Analyzing Robustness of Therapies) technique as a platform that integrates experimental data, mathematical modeling, and statistical analyses for identifying robust optimal treatment protocols. VEPART begins with time course experimental data for a sample population, and a mathematical model fit to aggregate data from that sample population. Using nonparametric statistics, the sample population is amplified and used to create a large number of virtual populations. At the final step of VEPART, robustness is assessed by identifying and analyzing the optimal therapy (perhaps restricted to a set of clinically realizable protocols) across each virtual population. As proof of concept, we have applied the VEPART method to study the robustness of treatment response in a mouse model of melanoma subject to treatment with immunostimulatory oncolytic viruses and dendritic cell vaccines. Our analysis (i) showed that every scheduling variant of the experimentally used treatment protocol is fragile (nonrobust) and (ii) discovered an alternative region of dosing space (lower oncolytic virus dose, higher dendritic cell dose) for which a robust optimal protocol exists.

A simple and rapid LC-MS/MS method for therapeutic drug monitoring of cetuximab: a GPCO-UNICANCER proof of concept study in head-and-neck cancer patients

Administration of first-in-class anti-EGFR monoclonal antibody cetuximab is contingent upon extensive pharmacogenomic testing. However in addition to tumor genomics, drug exposure levels could play a critical, yet largely underestimated role, because several reports have demonstrated that cetuximab pharmacokinetic parameters, in particular clearance values, were associated with survival in patients. Here, we have developed an original bioanalytical method based upon the use of LC-MS/MS technology and a simplified sample preparation procedure to assay cetuximab in plasma samples from patients, thus meeting the requirements of standard Therapeutic Drug Monitoring in routine clinical practice. When tested prospectively in a pilot study in 25 head-and-neck cancer patients, this method showed that patients with clinical benefit had cetixumab residual concentrations higher than non-responding patients (i.e., 49 ± 16.3 µg/ml VS. 25.8 ± 17 µg/ml, p < 0.01 t test). Further ROC analysis showed that 33.8 µg/ml was the Cmin threshold predictive of response with an acceptable sensitivity (87%) and specificity (78%). Mass spectrometry-based therapeutic drug monitoring of cetuximab in head-and-neck cancer patients could therefore help to rapidly predict cetuximab efficacy and to adapt dosing if required.

The Significance of an Enhanced Concept of the Organism for Medicine

Recent developments in evolutionary biology, comparative embryology, and systems biology suggest the necessity of a conceptual shift in the way we think about organisms. It is becoming increasingly evident that molecular and genetic processes are subject to extremely refined regulation and control by the cell and the organism, so that it becomes hard to define single molecular functions or certain genes as primary causes of specific processes. Rather, the molecular level is integrated into highly regulated networks within the respective systems. This has consequences for medical research in general, especially for the basic concept of personalized medicine or precision medicine. Here an integrative systems concept is proposed that describes the organism as a multilevel, highly flexible, adaptable, and, in this sense, autonomous basis for a human individual. The hypothesis is developed that these properties of the organism, gained from scientific observation, will gradually make it necessary to rethink the conceptual framework of physiology and pathophysiology in medicine.

Other side of the coin for personalised medicine and healthcare: content analysis of 'personalised' practices in the literature

OBJECTIVES

Various terms and definitions are used to describe personalised approaches to medicine and healthcare, but in ambiguous and inconsistent ways. They mostly have been defined in a top-down manner. However, actual practices might take different paths. Here, we aimed to provide a 'practice-based' perspective on the debate by analysing the content of 'personalised' practices published in the literature.

METHODS

The search in PubMed and EMBASE (April 2014) using the terms frequently used for personalised approaches resulted in 5333 records. 2 independent researchers used different strategies for screening, resulting in 157 articles describing 88 'personalised' practices that were implemented/presented on at least 1 individual/patient case. The content analysis was grounded on these data and did not have a priori analytical frameworks.

RESULTS

'Personalised medicine/healthcare' can be a commodity in the healthcare market, a way how health services are provided, or a keyword for emerging applications. It can help individuals/patients to gain control of their health, health professionals to provide better services, healthcare organisations to increase effectiveness and efficiency, or national health systems to increase performance. Country examples indicated that for integration of practices into health services, attitude towards innovations and health system and policy context is important. Categorisation based on the terms or the technologies used, if any, was not possible.

CONCLUSIONS

This study is the first to provide a comprehensive content analysis of the 'personalised' practices in the literature. Unlike the top-down definitions, our findings highlighted not the technologies but real-life issues faced by the practices. 'Personalised medicine' and 'personalised healthcare' can be differentiated by using the former for specific tools available and the latter for health services with a holistic approach, implemented in certain contexts. To realise integration of 'personalised medicine/healthcare' into real life, science, technology, health policy and practice, and society domains must work together.

Monoclonal antibodies for treating gastric cancer: promises and pitfalls

INTRODUCTION

Gastric cancer (GC) presents dismal prognosis when diagnosed at advanced stages, standard chemotherapy having shown little efficacy. Introduction of biotherapies interfering with novel targets and signaling pathways is currently an emerging strategy.

AREAS COVERED

Only two monoclonal antibodies (trastuzumab and ramucirumab) have been approved, mostly in association with cytotoxics. Conversely, testing other promising biotherapies (panitumumab, cetuximab, bevacizumab, rilotumumab) have yielded conflicting results, since encouraging early clinical trials have failed to be confirmed in larger phase-III studies. Empirical and underpowered strategies when designing combinational studies, lack of comprehensive knowledge of pharmacokinetics/pharmacodynamics (PK/PD) relationships, and underestimation of the large inter-patient variability in drug exposure levels with monoclonal antibodies, could explain the failures in developing biotherapies in gastric cancer. This review covers the achievements and limits of monoclonal antibodies in gastric cancer and proposes clues to overcome current failures.

EXPERT OPINION

Trastuzumab efficacy could be improved thanks to its combination with triplet chemotherapy or with another anti-HER2 agents or in continuation during second-line chemotherapy. Concerning ramucirumab, further studies are necessary to prove its interest in first line treatment of advanced GC, to use the optimal dose in each patient-given the large inter-patients variability, and to find predictive biomarkers of efficacy.

Toward precision medicine of breast cancer

In this review, we report on breast cancer's molecular features and on how high throughput technologies are helping in understanding the dynamics of tumorigenesis and cancer progression with the aim of developing precision medicine methods. We first address the current state of the art in breast cancer therapies and challenges in order to progress towards its cure. Then, we show how the interaction of high-throughput technologies with in silico modeling has led to set up useful inferences for promising strategies of target-specific therapies with low secondary effect incidence for patients. Finally, we discuss the challenge of pharmacogenetics in the clinical practice of cancer therapy. All these issues are explored within the context of precision medicine.

Potential crosstalk between cofilin-1 and EGFR pathways in cisplatin resistance of non-small-cell lung cancer

Current challenge in oncology is to establish the concept of personalized medicine in clinical practice. In this context, non-small-cell lung cancer (NSCLC) presents clinical, histological and molecular heterogeneity, being one of the most genomically diverse of all cancers. Recent advances added Epidermal Growth Factor Receptor (EGFR) as a predictive biomarker for patients with advanced NSCLC. In tumors with activating EGFR mutations, tyrosine kinase inhibitors (TKI) are indicated as first-line treatment, although restricted to a very small target population. In this context, cofilin-1 (a cytosolic protein involved with actin dynamics) has been widely studied as a biomarker of an aggressive phenotype in tumors, and overexpression of cofilin-1 is associated with cisplatin resistance and poor prognosis in NSCLC. Here, we gather information about the predictive potential of cofilin-1 and reviewed the crosstalk between cofilin-1/EGFR pathways. We aimed to highlight new perspectives of how these interactions might affect cisplatin resistance in NSCLC. We propose that cofilin-1 quantification in clinical samples in combination with presence/absence of EGFR mutation could be used to select patients that would benefit from TKI's treatment. This information is of paramount importance and could result in a possibility of guiding more effective treatments to NSCLC patients.

Plasminogen Activator System and Breast Cancer: Potential Role in Therapy Decision Making and Precision Medicine

Shifting from the historical TNM paradigm to the determination of molecular and genetic subtypes of tumors has been a major improvement to better picture cancerous diseases. The sharper the picture is, the better will be the possibility to develop subsequent strategies, thus achieving higher efficacy and prolonged survival eventually. Recent studies suggest that urokinase-type plasminogen activator (uPA), uPA Receptor (uPAR), and plasmino-gen activator inhibitor-1 (PAI-1) may play a critical role in cancer invasion and metastasis. Consistent with their role in cancer dissemination, high levels of uPA, PAI-1, and uPAR in multiple cancer types correlate with dismal prognosis. In this respect, upfront determination of uPA and PAI-1 as invasion markers has further opened up the possibilities for individualized therapy of breast cancer. Indeed, uPA and PAI-1 could help to classify patients on their risk for metastatic spreading and subsequent relapse, thus helping clinicians in their decision-making process to propose, or not propose, adjuvant therapy. This review covers the implications for cancer diagnosis, prognosis, and therapy of uPA and PAI-1, and therefore how they could be major actors in the development of a precision medicine in breast cancer.

Yin and yang of cytidine deaminase roles in clinical response to azacitidine in the elderly: a pharmacogenetics tale

Azacitidine is a mainstay for treating hematological disorders. Azacitidine is metabolized by cytidine deaminase, coded by a highly polymorphic gene. Here, we present two elderly patients with opposite clinical outcomes after azacitidine treatment. First, an acute myeloid leukemia patient showed life-threatening toxicities, but outstanding complete remission, after a single round of azacitidine. Further investigations showed that this patient was cytidine deaminase 79A>C (rs2072671) homozygous with a marked deficient phenotype. Next, a chronic myelomonocytic leukemia patient displayed complete lack of response despite several cycles of azacitidine. This patient had a rapid-deaminator phenotype linked to the -31delC deletion (rs3215400). These polymorphisms lead to opposite clinical outcomes in patients with myelodysplastic syndromes treated with azacitidine, thus suggesting that determining cytidine deaminase status could help to forecast clinical outcome.

Pharmacogenetics of cancer therapy: breakthroughs from beyond?

‘Pharmacogenetics or Pharmacogenomics’ (PG) is one of the most practiced cancer therapeutic strategies, tailored for individualized patients. Despite its popularity and rapid advancements in the field, many obstacles for cancer therapy PG still need to be overcome. By borrowing scientific systems from other disciplines such as cancer diagnosis, and therapeutic information from the diversity of tumor origins, categories and stages, cancer therapy PG may hopefully be improved. Furthermore, to quickly acquire genetic and pathologic information and seek therapeutic interventions, possible breakthroughs may come from beyond – changing the cancer therapeutic landscapes. The next generations of PG protocols and hospital routines for searching deadly cancer pathogenic pathways versus drug-targeting predictions are of great clinical significance for the future. Yet, progress of cancer therapy PG is entering into a bottleneck stage owing to simple model of relevant techniques and routines. Promoting or even innovating present PG modular is very necessary. This perspective highlights this issue by introducing new initiatives and ideas.

The cost-effectiveness of UGT1A1 genotyping before colorectal cancer treatment with irinotecan from the perspective of the German statutory health insurance

BACKGROUND

The evidence concerning the cost-effectiveness of UGT1A1*28 genotyping is ambiguous and does not allow drawing valid conclusions for Germany. This study evaluates the cost-effectiveness of UGT1A1 genotyping in patients with metastatic colorectal cancer undergoing irinotecan-based chemotherapy compared to no testing from the perspective of the German statutory health insurance.

MATERIAL AND METHODS

A decision-analytic Markov model with a life time horizon was developed. No testing was compared to two genotype-dependent therapy strategies: 1) dose reduction by 25%; and 2) administration of a prophylactic G-CSF growth factor analog for homozygous and heterozygous patients. Probability, quality of life and cost parameters used in this study were based on published literature. Deterministic and probabilistic sensitivity analyses were performed to account for parameter uncertainties.

RESULTS

Strategy 1 dominated all remaining strategies. Compared to no testing, it resulted in only marginal QALY increases (0.0002) but a cost reduction of €580 per patient. Strategy 2 resulted in the same health gains but increased costs by €10 773. In the probabilistic analysis, genotyping and dose reduction was the optimal strategy in approximately 100% of simulations at a threshold of €50 000 per QALY. Deterministic sensitivity analysis shows that uncertainty for this strategy originated primarily from costs for irinotecan-based chemotherapy, from the prevalence of neutropenia among heterozygous patients, and from whether dose reduction is applied to both homozygotes and heterozygotes or only to the former.

CONCLUSION

This model-based synthesis of the most recent evidence suggests that pharmacogenetic UGT1A1 testing prior to irinotecan-based chemotherapy dominates non-personalized colon cancer care in Germany. However, as structural uncertainty remains high, these results require validation in clinical practice, e.g. based on a managed-entry agreement.

Pharmacogenetics and breast cancer management: current status and perspectives

INTRODUCTION

Breast cancer has benefited from a number of innovative therapeutics over the last decade. Cytotoxics, hormone therapy, targeted therapies and biologics can now be given to ensure optimal management of patients. As life expectancy of breast cancer patients has been significantly stretched and that several lines of treatment are now made available, determining the best drug or drug combinations to be primarily given and the best dosing and scheduling for each patient is critical for ensuring an optimal toxicity/efficacy balance.

AREAS COVERED

Defining patient's characteristics at the tumor level (pharmacogenomics) and the constitutional level (pharmacogenetics) is a rising trend in oncology. This review covers the latest strategies based upon the search of relevant biomarkers for efficacy, resistance and toxicity to be undertaken at the bedside to shift towards precision medicine in breast cancer patients.

EXPERT OPINION

In the expanding era of bioguided medicine, identifying relevant and clinically validated biomarkers from the plethora of published material remains an uneasy task. Sorting the variety of genetic and molecular markers that have been investigated over the last decade on their level of evidence and addressing the issue of drug exposure should help to improve the management of breast cancer therapy.

Role of cytidine deaminase in toxicity and efficacy of nucleosidic analogs

INTRODUCTION

Nucleosidic analogs such as pyrimidine and purine derivatives are mainstay in the field of treating cancers, both in adults and in children. All these drugs act as antimetabolite compounds, that is, they interfere with the ability of cancer cells to synthesize the nucleosides or the nucleotides necessary for proliferation and progression. As with most cytotoxics, maintaining patients in their therapeutic window is challenging, and predicting changes in drug exposure is critical to ensure an optimal efficacy/toxicity balance.

AREAS COVERED

Among the antimetabolites, a small but widely prescribed number of drugs (i.e., gemcitabine, capecitabine, cytarabine, azacytidine) share a same metabolic pattern driven by a liver enzyme, cytidine deaminase (CDA), coded by a gene displaying several genetic and epigenetic polymorphisms. Consequently, CDA activity is erratic, ranging from deficient to ultra-rapid deaminator patients, with subsequent impact on drug pharmacokinetics and pharmacodynamics eventually. This review provides an update on the variety of clinical studies and case-reports investigating on CDA status as a marker for clinical outcome in cancer patients treated with nucleosidic analogs.

EXPERT OPINION

Whereas sorting patients on the basis of their CDA genotype remains tricky because of unclear genotype-to-phenotype relationships, developing functional strategies (i.e., phenotype-based status determination) could help to use CDA status as a biomarker for developing adaptive dosing strategies with nucleosidic analogs.

Molecular profiling and companion diagnostics: where is personalized medicine in cancer heading?

The goal of personalized medicine is to use the right drug at the right dose - with minimal or no toxicity - for the right patient at the right time. Recent advances in understanding cell biology and pathways, and in using molecular 'omics' technologies to diagnose cancer, offer a strategic bridge to personalized medicine in cancer. Modern personalized medicine takes into account an individual's genetic makeup and disease history before developing a treatment regimen. The future of clinical oncology will be based on the use of predictive and prognostic biomarkers in patient management. Once implemented widely, personalized medicine will benefit patients and the healthcare system greatly.

Malignant pleural mesothelioma: current and future perspectives

Mesothelioma still remains an occupational related cancer with severe outcome. It is usually diagnosed at advanced stage since it does not demonstrate early symptoms. Several efforts have been made towards removing all materials inducing mesothelioma in the work setting and new work protection measures have been applied. Although we have new targeted treatments and radical surgery as arrows in the quiver, the type of mesothelioma and early diagnosis still remain the best treatment approach. Novel treatment modalities have been explored and several others are already on the way. In the current review we will present current data for mesothelioma and future perspectives.

High-throughput, multiparameter analysis of single cells

Heterogeneity of cell populations in various biological systems has been widely recognized, and the highly heterogeneous nature of cancer cells has been emerging with clinical relevance. Single-cell analysis using a combination of high-throughput and multiparameter approaches is capable of reflecting cell-to-cell variability, and at the same time of unraveling the complexity and interdependence of cellular processes in the individual cells of a heterogeneous population. In this review, analytical methods and microfluidic tools commonly used for high-throughput, multiparameter single-cell analysis of DNA, RNA, and proteins are discussed. Applications and limitations of currently available technologies for cancer research and diagnostics are reviewed in the light of the ultimate goal to establish clinically applicable assays.

Systems analysis of oxidant stress in the vasculature

Systems biology and network analysis are emerging as valuable tools for the discovery of novel relationships, the identification of key regulatory factors, and the prediction of phenotypic changes in complex biological systems. Redox homeostasis in the vasculature is maintained by an intricate balance between oxidant-generating and antioxidant systems. When these systems are perturbed, conditions are permissive for oxidant stress, which, in turn, promotes vascular dysfunction and structural remodeling. Owing to the number of elements involved in redox regulation and the different vascular pathophenotypes associated with oxidant stress, vascular oxidant stress represents an ideal system to study by network analysis. Networks offer a method to organize experimentally derived factors, including proteins, metabolites, and DNA, that are represented as nodes into an unbiased comprehensive platform for study. Through analysis of the network, it is possible to determine essential or regulatory nodes, identify previously unknown connections between nodes, and locate modules, which are groups of nodes located within the same neighborhood that function together and have implications for phenotype. Investigators have only recently begun to construct oxidant stress-related networks to examine vascular structure and function; however, these early studies have provided mechanistic insight to further our understanding of this complicated biological system.