Breast cancer assessment tools and optimizing adjuvant therapy

Prognostic models for breast cancer: based on logistics regression and Hybrid Bayesian Network

BACKGROUND

To construct two prognostic models to predict survival in breast cancer patients; to compare the efficacy of the two models in the whole group and the advanced human epidermal growth factor receptor-2-positive (HER2+) subgroup of patients; to conclude whether the Hybrid Bayesian Network (HBN) model outperformed the logistics regression (LR) model.

METHODS

In this paper, breast cancer patient data were collected from the SEER database. Data processing and analysis were performed using Rstudio 4.2.0, including data preprocessing, model construction and validation. The L_DVBN algorithm in Julia0.4.7 and bnlearn package in R was used to build and evaluate the HBN model. Data with a diagnosis time of 2018(n = 23,384) were distributed randomly as training and testing sets in the ratio of 7:3 using the leave-out method for model construction and internal validation. External validation of the model was done using the dataset of 2019(n = 8128). Finally, the late HER2 + patients(n = 395) was selected for subgroup analysis. Accuracy, calibration and net benefit of clinical decision making were evaluated for both models.

RESULTS

The HBN model showed that seventeen variables were associated with survival outcome, including age, tumor size, site, histologic type, radiotherapy, surgery, chemotherapy, distant metastasis, subtype, clinical stage, ER receptor, PR receptor, clinical grade, race, marital status, tumor laterality, and lymph node. The AUCs for the internal validation of the LR and HBN models were 0.831 and 0.900; The AUCs for the external validation of the LR and HBN models on the whole population were 0.786 and 0.871; the AUCs for the external validation of the two models on the subgroup population were 0.601 and 0.813.

CONCLUSION

The accuracy, net clinical benefit, and calibration of the HBN model were better than LR model. The predictive efficacy of both models decreased and the difference was greater in advanced HER2 + patients, which means the HBN model had higher robustness and more stable predictive performance in the subgroup.

The role of LncRNA LBX2-AS1 in cancers: functions, mechanisms and potential clinical utility

Increasingly advanced biology technique has revealed that long non-coding RNAs (lncRNA) as critical factors that exert significant regulatory effects on biological functions by modulating gene transcription, epigenetic modifications and protein translation. A newly emerging lncRNA, ladybird homeobox 2 (LBX2)-antisense RNA 1 (LBX2-AS1), was found to be highly expressed in various tumors. Moreover, it is functionally linked to the regulation of essential tumor-related biological processes, such as cell proliferation and apoptosis, through interactions with multiple signaling molecules/pathways. The important roles played by LBX2-AS1 in cancer initiation and progression suggest that this lncRNA has enormous clinical potential for use as a novel biomarker or therapeutic target. In this article, we retrospectively review the latest advances in research exploring the roles of the lncRNA LBX2-AS1 in oncology field, highlighting its involvement in a comprehensive network of molecular mechanisms underlying diverse cancers and examining its potential applications in clinical practice.

Lipid-Based Nanomaterials for Drug Delivery Systems in Breast Cancer Therapy

Globally, breast cancer is one of the most prevalent diseases, inducing critical intimidation to human health. Lipid-based nanomaterials have been successfully demonstrated as drug carriers for breast cancer treatment. To date, the development of a better drug delivery system based on lipid nanomaterials is still urgent to make the treatment and diagnosis easily accessible to breast cancer patients. In a drug delivery system, lipid nanomaterials have revealed distinctive features, including high biocompatibility and efficient drug delivery. Specifically, a targeted drug delivery system based on lipid nanomaterials has inherited the advantage of optimum dosage and low side effects. In this review, insights on currently used potential lipid-based nanomaterials are collected and introduced. The review sheds light on conjugation, targeting, diagnosis, treatment, and clinical significance of lipid-based nanomaterials to treat breast cancer. Furthermore, a brighter side of lipid-based nanomaterials as future potential drug delivery systems for breast cancer therapy is discussed.

miR-499a inhibits the proliferation and apoptosis of prostate cancer via targeting UBE2V2

BACKGROUND

Prostate cancer is one of the malignant tumors of the urinary system and ranks second among the fatal cancers in men. And with age, the incidence of prostate cancer will increase linearly.

METHODS

In this study, we measured the expression of Ubiquitin Conjugating Enzyme E2 V2 (UBE2V2) in prostate cancer tissues and cell lines by WB and explored the effect of UBE2V2 on the proliferation characteristics of prostate cancer by MTT and colony formation test.

RESULTS

In our research, we found that the UBE2V2 protein level in prostate cancer cell lines was significantly higher than the UBE2V2 protein level in normal prostate cells, and the mRNA expression level did not change significantly compared with normal prostate tissue cells. At the same time, we found that miR-499a combined with UBE2V2 inhibited the expression of UBE2V2 in prostate cancer cells.

CONCLUSIONS

In conclusion, our results indicate that miR-499a inhibits the proliferation of human prostate cancer cells by targeting UBE2V2, which will provide a potential target for the treatment of prostate cancer.

Propensity score analysis of the prognostic value of genomic assays for breast cancer in diverse populations using the National Cancer Data Base

BACKGROUND

Genomic assays such as Oncotype Dx (ODX) and MammaPrint are used for risk-adapted treatment decisions among patients with early breast cancer. However, to the authors' knowledge, concordance between genomic assays is modest. Using real-world data, the authors performed a comparative analysis of ODX and MammaPrint.

METHODS

A cohort of women diagnosed with early-stage, hormone receptor-positive breast cancer who received ODX or MammaPrint was established using the National Cancer Data Base (NCDB) for 2010 through 2016. Using the propensity score matching method, 2 groups of patients with similar clinical and demographic characteristics were defined: one group received ODX and the other received MammaPrint. The authors examined the association between use of the ODX or MammaPrint assays and overall survival using Cox models.

RESULTS

Of the 451,693 eligible patients, approximately 45.3% received ODX and 1.8% received MammaPrint testing. The use of ODX increased from 36.1% in 2010 to 49.9% in 2016, whereas use of MammaPrint increased from 0.5% in 2010 to 3.3% in 2016. The authors matched 5042 patients who received ODX with 5042 patients who received MammaPrint. The 5-year risks of death for the MammaPrint low-risk group and the ODX low-risk group were 3.4% and 4.7%, respectively. The prognostic value of MammaPrint was similar to that of ODX; the C-index was 0.614 (95% confidence interval, 0.572-0.657) for MammaPrint and 0.581 (95% confidence interval, 0.530-0.631) for ODX. There was a difference in the performance of the ODX assay observed across racial and/or ethnic groups (P < .001), with a slightly better performance noted among white compared with African American and Hispanic individuals.

CONCLUSIONS

Both the ODX and MammaPrint tests are good at identifying low-risk individuals who could be spared chemotherapy. The suboptimal performance of ODX in ethnic minority individuals deserves further investigation.

Breast cancer prognosis signature: linking risk stratification to disease subtypes

Breast cancer is a very complex and heterogeneous disease with variable molecular mechanisms of carcinogenesis and clinical behaviors. The identification of prognostic risk factors may enable effective diagnosis and treatment of breast cancer. In particular, numerous gene-expression-based prognostic signatures were developed and some of them have already been applied into clinical trials and practice. In this study, we summarized several representative gene-expression-based signatures with significant prognostic value and separately assessed their ability of prognosis prediction in their originally targeted populations of breast cancer. Notably, many of the collected signatures were originally designed to predict the outcomes of estrogen receptor positive (ER+) patients or the whole breast cancer cohort; there are no typical signatures used for the prognostic prediction in a specific population of patients with the intrinsic subtype. We thus attempted to identify subtype-specific prognostic signatures via a computational framework for analyzing multi-omics profiles and patient survival. For both the discovery and an independent data set, we confirmed that subtype-specific signature is a strong and significant independent prognostic factor in the corresponding cohort. These results indicate that the subtype-specific prognostic signature has a much higher resolution in the risk stratification, which may lead to improved therapies and precision medicine for patients with breast cancer.

Pyrvinium Pamoate Induces Death of Triple-Negative Breast Cancer Stem-Like Cells and Reduces Metastases through Effects on Lipid Anabolism

Cancer stem-like cells (CSC) induce aggressive tumor phenotypes such as metastasis formation, which is associated with poor prognosis in triple-negative breast cancer (TNBC). Repurposing of FDA-approved drugs that can eradicate the CSC subcompartment in primary tumors may prevent metastatic disease, thus representing an effective strategy to improve the prognosis of TNBC. Here, we investigated spheroid-forming cells in a metastatic TNBC model. This strategy enabled us to specifically study a population of long-lived tumor cells enriched in CSCs, which show stem-like characteristics and induce metastases. To repurpose FDA-approved drugs potentially toxic for CSCs, we focused on pyrvinium pamoate (PP), an anthelmintic drug with documented anticancer activity in preclinical models. PP induced cytotoxic effects in CSCs and prevented metastasis formation. Mechanistically, the cell killing effects of PP were a result of inhibition of lipid anabolism and, more specifically, the impairment of anabolic flux from glucose to cholesterol and fatty acids. CSCs were strongly dependent upon activation of lipid biosynthetic pathways; activation of these pathways exhibited an unfavorable prognostic value in a cohort of breast cancer patients, where it predicted high probability of metastatic dissemination and tumor relapse. Overall, this work describes a new approach to target aggressive CSCs that may substantially improve clinical outcomes for patients with TNBC, who currently lack effective targeted therapeutic options. SIGNIFICANCE: These findings provide preclinical evidence that a drug repurposing approach to prevent metastatic disease in TNBC exploits lipid anabolism as a metabolic vulnerability against CSCs in primary tumors.

Adjuvant Chemotherapy for Low-Clinical-Risk Breast Cancer Defined by Modified Version of Adjuvant! Online: A Propensity Score Matched SEER Analysis

Background

The purpose of this research was to investigate whether the modified version of Adjuvant! Online was able to omit chemotherapy (CT) for patients with hormone receptor (HR)-positive, human epidermal growth factor receptor 2 (HER2)-negative, and axillary node-negative breast cancer, who are defined as low clinical risk.

Methods

From 2010 to 2014, HR-positive, HER2-negative, and node-negative breast cancer patients aged 50 years and older were retrieved from the Surveillance, Epidemiology, and End Results (SEER) 18 database. The propensity score matching method was applied between the no-CT and CT groups. Overall survival (OS) was evaluated using Kaplan-Meier analysis and compared across groups using a log-rank test.

Results

A total of 48,857 patients were enrolled. After propensity score matching, the numbers of patients in the no-CT and CT groups were both 3,102. The median follow-up period was 37 months. The 5-year OS rates in the no-CT and CT groups were 92 and 91%, respectively (p = 0.066). In the subgroup with a tumor score (tumor size added to tumor grade) of 2-3, OS was significantly higher in the no-CT group than in the CT group (93 vs. 90%, p < 0.001). In the subgroup with a tumor score of 4, OS was not different between these 2 groups (92 vs. 93%, p = 0.47).

Conclusion

This retrospective study provides evidence that CT may not be beneficial to patients 50 years of age or older with HR-positive, HER2-negative, axillary node-negative breast cancer and additionally defined as low clinical risk by a modified version of Adjuvant! Online.

Rapid Microwave-Assisted Cisplatin-Loaded Solid Lipid Nanoparticles: Synthesis, Characterization and Anticancer Study

Cisplatin is one of the most leading potent chemotherapy drugs prescribed for the treatment of most solid tumors. However, the induction of toxicities and the development of resistance restricts its applications. Efforts are made in the proposed study to control the delivery of cisplatin to tumor sites by incorporating it into solid lipid nanoparticle (SLNs) drug carriers. By considering this fact, in the current research work, a single-step, one-pot, microwave-assisted technology was used to produce cisplatin-loaded SLNs. The shape of the SLNs was observed to be spherical, with a uniform size distribution of 74.85 nm, polydispersity index (PDI) of 0.311, and zeta potential of -20.8 mV. The percentage of encapsulation efficiency was found to be 71.85%. In vitro drug release study was calculated to be 80% in 24 h. The formulation in blood was found to be safe; a study of hemolysis confirmed this. Breast cancer cell line MCF-7 was used to test cytotoxicity and cellular interaction of cisplatin-loaded SLNs with an IC₅₀ value of 6.51 ± 0.39 μg/mL. Overall, the results of our findings show that the approach of SLNs-based, cisplatin-based, drug delivery has led to increased sustainability in breast cancer therapy with superior biocompatibility.

Development and Characterization of Nanoliposomal Hydroxyurea Against BT-474 Breast Cancer Cells

Purpose: Hydroxyurea (HU) is a well-known chemotherapy drug with several side effects which limit its clinical application. This study was conducted to improve its therapeutic efficiency against breast cancer using liposomes as FDA-approved drug carriers.

Methods: PEGylated nanoliposomes-containing HU (NL-HU) were made via a thin-film hydration method, and assessed in terms of zeta potential, size, morphology, release, stability, cellular uptake, and cytotoxicity. The particle size and zeta potential of NL-HU were specified by zeta-sizer. The drug release from liposomes was assessed by dialysis diffusion method. Cellular uptake was evaluated by flow cytometry. The cytotoxicity was designated by methyl thiazolyl diphenyl-tetrazolium bromide (MTT) test.

Results: The size and zeta value of NL-HU were gotten as 85 nm and -27 mV, respectively. NL-HU were spherical.NL-HU vesicles were detected to be stable for two months. The slow drug release and Weibull kinetic model were obtained. Liposomes considerably enhanced the uptake of HU into BT-474 human breast cancer cells. The cytotoxicity of NL-HU on BT-474 cells was found to be significantly more than that of free HU.

Conclusion: The results confirmed these PEGylated nanoliposomes containing drug are potentially suitable against in vitro model of breast cancer.

Computational identification of mutator-derived lncRNA signatures of genome instability for improving the clinical outcome of cancers: a case study in breast cancer

Emerging evidence revealed the critical roles of long non-coding RNAs (lncRNAs) in maintaining genomic instability. However, identification of genome instability-associated lncRNAs and their clinical significance in cancers remain largely unexplored. Here, we developed a mutator hypothesis-derived computational frame combining lncRNA expression profiles and somatic mutation profiles in a tumor genome and identified 128 novel genomic instability-associated lncRNAs in breast cancer as a case study. We then identified a genome instability-derived two lncRNA-based gene signature (GILncSig) that stratified patients into high- and low-risk groups with significantly different outcome and was further validated in multiple independent patient cohorts. Furthermore, the GILncSig correlated with genomic mutation rate in both ovarian cancer and breast cancer, indicating its potential as a measurement of the degree of genome instability. The GILncSig was able to divide TP53 wide-type patients into two risk groups, with the low-risk group showing significantly improved outcome and the high-risk group showing no significant difference compared with those with TP53 mutation. In summary, this study provided a critical approach and resource for further studies examining the role of lncRNAs in genome instability and introduced a potential new avenue for identifying genomic instability-associated cancer biomarkers.

Development of tumor mutation burden as an immunotherapy biomarker: utility for the oncology clinic

Background

Treatment with immune checkpoint blockade (ICB) with agents such as anti-programmed cell death protein 1 (PD-1), anti-programmed death-ligand 1 (PD-L1), and/or anti-cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) can result in impressive response rates and durable disease remission but only in a subset of patients with cancer. Expression of PD-L1 has demonstrated utility in selecting patients for response to ICB and has proven to be an important biomarker for patient selection. Tumor mutation burden (TMB) is emerging as a potential biomarker. However, refinement of interpretation and contextualization is required.

Materials and methods

In this review, we outline the evolution of TMB as a biomarker in oncology, delineate how TMB can be applied in the clinic, discuss current limitations as a diagnostic test, and highlight mechanistic insights unveiled by the study of TMB. We review available data to date studying TMB as a biomarker for response to ICB by tumor type, focusing on studies proposing a threshold for TMB as a predictive biomarker for ICB activity.

Results

High TMB consistently selects for benefit with ICB therapy. In lung, bladder and head and neck cancers, the current predictive TMB thresholds proposed approximate 200 non-synonymous somatic mutations by whole exome sequencing (WES). PD-L1 expression influences response to ICB in high TMB tumors with single agent PD-(L)1 antibodies; however, response may not be dependent on PD-L1 expression in the setting of anti-CTLA4 or anti-PD-1/CTLA-4 combination therapy. Disease-specific TMB thresholds for effective prediction of response in various other malignancies are not well established.

Conclusions

TMB, in concert with PD-L1 expression, has been demonstrated to be a useful biomarker for ICB selection across some cancer types; however, further prospective validation studies are required. TMB determination by selected targeted panels has been correlated with WES. Calibration and harmonization will be required for optimal utility and alignment across all platforms currently used internationally. Key challenges will need to be addressed before broader use in different tumor types.

Do online prognostication tools represent a valid alternative to genomic profiling in the context of adjuvant treatment of early breast cancer? A systematic review of the literature

INTRODUCTION

Decision-making regarding adjuvant chemotherapy has been based on clinical and pathological features. However, such decisions are seldom consistent. Web-based predictive models have been developed using data from cancer registries to help determine the need for adjuvant therapy. More recently, with the recognition of the heterogenous nature of breast cancer, genomic assays have been developed to aid in the therapeutic decision-making.

METHODS

We have carried out a comprehensive literature review regarding online prognostication tools and genomic assays to assess whether online tools could be used as valid alternatives to genomic profiling in decision-making regarding adjuvant therapy in early breast cancer.

RESULTS AND CONCLUSIONS

Breast cancer has been recently recognized as a heterogenous disease based on variations in molecular characteristics. Online tools are valuable in guiding adjuvant treatment, especially in resource constrained countries. However, in the era of personalized therapy, molecular profiling appears to be superior in predicting clinical outcome and guiding therapy.

Personalized chemotherapy selection for breast cancer using gene expression profiles

Choosing the optimal chemotherapy regimen is still an unmet medical need for breast cancer patients. In this study, we reanalyzed data from seven independent data sets with totally 1079 breast cancer patients. The patients were treated with three different types of commonly used neoadjuvant chemotherapies: anthracycline alone, anthracycline plus paclitaxel, and anthracycline plus docetaxel. We developed random forest models with variable selection using both genetic and clinical variables to predict the response of a patient using pCR (pathological complete response) as the measure of response. The models were then used to reassign an optimal regimen to each patient to maximize the chance of pCR. An independent validation was performed where each independent study was left out during model building and later used for validation. The expected pCR rates of our method are significantly higher than the rates of the best treatments for all the seven independent studies. A validation study on 21 breast cancer cell lines showed that our prediction agrees with their drug-sensitivity profiles. In conclusion, the new strategy, called PRES (Personalized REgimen Selection), may significantly increase response rates for breast cancer patients, especially those with HER2 and ER negative tumors, who will receive one of the widely-accepted chemotherapy regimens.

The recurrent architecture of tumour initiation, progression and drug sensitivity

Recent studies across multiple tumour types are starting to reveal a recurrent regulatory architecture in which genomic alterations cluster upstream of functional master regulator (MR) proteins, the aberrant activity of which is both necessary and sufficient to maintain tumour cell state. These proteins form small, hyperconnected and autoregulated modules (termed tumour checkpoints) that are increasingly emerging as optimal biomarkers and therapeutic targets. Crucially, as their activity is mostly dysregulated in a post-translational manner, rather than by mutations in their corresponding genes or by differential expression, the identification of MR proteins by conventional methods is challenging. In this Opinion article, we discuss novel methods for the systematic analysis of MR proteins and of the modular regulatory architecture they implement, including their use as a valuable reductionist framework to study the genetic heterogeneity of human disease and to drive key translational applications.

SIAH and EGFR, Two RAS Pathway Biomarkers, are Highly Prognostic in Locally Advanced and Metastatic Breast Cancer

BACKGROUND

Metastatic breast cancer exhibits diverse and rapidly evolving intra- and inter-tumor heterogeneity. Patients with similar clinical presentations often display distinct tumor responses to standard of care (SOC) therapies. Genome landscape studies indicate that EGFR/HER2/RAS "pathway" activation is highly prevalent in malignant breast cancers. The identification of therapy-responsive and prognostic biomarkers is paramount important to stratify patients and guide therapies in clinical oncology and personalized medicine.

METHODS

In this study, we analyzed matched pairs of tumor specimens collected from 182 patients who received neoadjuvant systemic therapies (NST). Statistical analyses were conducted to determine whether EGFR/HER2/RAS pathway biomarkers and clinicopathological predictors, alone and in combination, are prognostic in breast cancer.

FINDINGS

SIAH and EGFR outperform ER, PR, HER2 and Ki67 as two logical, sensitive and prognostic biomarkers in metastatic breast cancer. We found that increased SIAH and EGFR expression correlated with advanced pathological stage and aggressive molecular subtypes. Both SIAH expression post-NST and NST-induced changes in EGFR expression in invasive mammary tumors are associated with tumor regression and increased survival, whereas ER, PR, and HER2 were not. These results suggest that SIAH and EGFR are two prognostic biomarkers in breast cancer with lymph node metastases.

INTERPRETATION

The discovery of incorporating tumor heterogeneity-independent and growth-sensitive RAS pathway biomarkers, SIAH and EGFR, whose altered expression can be used to estimate therapeutic efficacy, detect emergence of resistant clones, forecast tumor regression, differentiate among partial responders, and predict patient survival in the neoadjuvant setting, has a clear clinical implication in personalizing breast cancer therapy.

FUNDING

This work was supported by the Dorothy G. Hoefer Foundation for Breast Cancer Research (A.H. Tang); Center for Innovative Technology (CIT)-Commonwealth Research Commercialization Fund (CRCF) (MF14S-009-LS to A.H. Tang), and National Cancer Institute (CA140550 to A.H. Tang).

70-Gene Signature as an Aid to Treatment Decisions in Early-Stage Breast Cancer

BACKGROUND

The 70-gene signature test (MammaPrint) has been shown to improve prediction of clinical outcome in women with early-stage breast cancer. We sought to provide prospective evidence of the clinical utility of the addition of the 70-gene signature to standard clinical-pathological criteria in selecting patients for adjuvant chemotherapy.

METHODS

In this randomized, phase 3 study, we enrolled 6693 women with early-stage breast cancer and determined their genomic risk (using the 70-gene signature) and their clinical risk (using a modified version of Adjuvant! Online). Women at low clinical and genomic risk did not receive chemotherapy, whereas those at high clinical and genomic risk did receive such therapy. In patients with discordant risk results, either the genomic risk or the clinical risk was used to determine the use of chemotherapy. The primary goal was to assess whether, among patients with high-risk clinical features and a low-risk gene-expression profile who did not receive chemotherapy, the lower boundary of the 95% confidence interval for the rate of 5-year survival without distant metastasis would be 92% (i.e., the noninferiority boundary) or higher.

RESULTS

A total of 1550 patients (23.2%) were deemed to be at high clinical risk and low genomic risk. At 5 years, the rate of survival without distant metastasis in this group was 94.7% (95% confidence interval, 92.5 to 96.2) among those not receiving chemotherapy. The absolute difference in this survival rate between these patients and those who received chemotherapy was 1.5 percentage points, with the rate being lower without chemotherapy. Similar rates of survival without distant metastasis were reported in the subgroup of patients who had estrogen-receptor-positive, human epidermal growth factor receptor 2-negative, and either node-negative or node-positive disease.

CONCLUSIONS

Among women with early-stage breast cancer who were at high clinical risk and low genomic risk for recurrence, the receipt of no chemotherapy on the basis of the 70-gene signature led to a 5-year rate of survival without distant metastasis that was 1.5 percentage points lower than the rate with chemotherapy. Given these findings, approximately 46% of women with breast cancer who are at high clinical risk might not require chemotherapy. (Funded by the European Commission Sixth Framework Program and others; ClinicalTrials.gov number, NCT00433589; EudraCT number, 2005-002625-31.).

FN14 and GRP94 expression are prognostic/predictive biomarkers of brain metastasis outcome that open up new therapeutic strategies

Brain metastasis is a devastating problem in patients with breast, lung and melanoma tumors. GRP94 and FN14 are predictive biomarkers over-expressed in primary breast carcinomas that metastasized in brain. To further validate these brain metastasis biomarkers, we performed a multicenter study including 318 patients with breast carcinomas. Among these patients, there were 138 patients with metastasis, of whom 84 had brain metastasis. The likelihood of developing brain metastasis increased by 5.24-fold (95%CI 2.83-9.71) and 2.55- (95%CI 1.52-4.3) in the presence of FN14 and GRP94, respectively. Moreover, FN14 was more sensitive than ErbB2 (38.27 vs. 24.68) with similar specificity (89.43 vs. 89.55) to predict brain metastasis and had identical prognostic value than triple negative patients (p < 0.0001). Furthermore, we used GRP94 and FN14 pathways and GUILD, a network-based disease-gene prioritization program, to pinpoint the genes likely to be therapeutic targets, which resulted in FN14 as the main modulator and thalidomide as the best scored drug. The treatment of mice with brain metastasis improves survival decreasing reactive astrocytes and angiogenesis, and down-regulate FN14 and its ligand TWEAK. In conclusion our results indicate that FN14 and GRP94 are prediction/prognosis markers which open up new possibilities for preventing/treating brain metastasis.

The Performance of the Nottingham Prognosis Index and the Adjuvant Online Decision Making Tool for Prognosis in Early-stage Breast Cancer Patients

Background:

Prognostic tools are widely used in the practice of Oncology and have been developed to help stratify patients into specific risk-related grouping. We sought to apply of two such tools used for patients with early-stage breast cancer and to correlate them with actual outcomes.

Methods:

A retrospective study was designed to include early-stage breast cancer cases seen from 1994 to 2014 at the Seyedoshohada Hospital in Isfahan, Iran. Information was derived from the patients’ records, and indices were derived from prognostic tools. Information was analyzed using descriptive statistics and one sample t-test.

Results:

In 233 patients, the difference between the predicted overall survival (OS) by the Adjuvant Online (AO) prognosis tools (69.28) and the observed OS (71.2) was not statistically significant (P = 0.52), and the AO prognosis tools had predicted the patients’ OS correctly. In the Nottingham prognosis index (NPI), this difference in all groups except the very poor prognosis group was not statistically significant.

Conclusions:

Adjuvant Online prognosis tools were capable of predicting the 10-year OS rate although not in all of the subgroups. The NPI was capable of distinguishing good, moderate, and poor survival rates, but this ability was not visible in more specific groups with moderate and poor prognosis.

Serum VEGF-A and Tumor Vessel VEGFR-2 Levels Predict Survival in Caucasian but Not Asian Patients Undergoing Resection for Gastric Adenocarcinoma

BACKGROUND

Clinical trials of agents targeting the vascular endothelial growth factor A (VEGF-A) pathway in gastric adenocarcinoma (GA) suggest that these therapies may have varying efficacy in different races.

METHODS

VEGF-A in serum and/or VEGF receptor 2 (VEGFR-2) in CD31-positive tumor vessels (VEGFR-2/CD31) were measured in 118 Caucasians and 263 Asians who underwent gastric resection at two institutions and correlated with overall survival (OS). Blood was drawn before any treatment. Patients receiving neoadjuvant treatment were excluded from VEGFR-2 analysis.

RESULTS

Compared with Asians, Caucasians were older (mean age 66-73 vs 59-62 years), had more proximal tumors, and had more advanced TNM stage. In the VEGF-A cohort, Caucasians had a median VEGF-A level that was 95 % higher than that of Asians and a much higher standard deviation (88 ± 6.206 vs 45 ± 76 pg/ml, p < 0.001). The 5-year OS for patients with low versus high VEGF-A levels was 72 versus 43 % in Caucasians (p = 0.001) and 86 versus 77 % in Asians (p = 0.236). In the VEGFR-2 cohort, OS was worse in Caucasians with high VEGFR-2/CD31 levels (49 vs 73 %, p = 0.038), while there was no significant difference in OS in Asians (80 vs 90 %, p = 0.119). On multivariate analyses of significant prognostic factors (excluding treatment factors and margin status), serum VEGF-A and tumor VEGFR-2/CD31 levels were independent predictors of OS only in Caucasians.

CONCLUSIONS

In patients with resectable GA, VEGF-A and VEGFR-2/CD31 levels are independent predictors of OS in Caucasians but not in Asians, suggesting varying importance of this pathway in GA progression among different races.

Shp2 signaling suppresses senescence in PyMT-induced mammary gland cancer in mice

In this study, we have used techniques from cell biology, biochemistry, and genetics to investigate the role of the tyrosine phosphatase Shp2 in tumor cells of MMTV-PyMT mouse mammary glands. Genetic ablation or pharmacological inhibition of Shp2 induces senescence, as determined by the activation of senescence-associated β-gal (SA-β-gal), cyclin-dependent kinase inhibitor 1B (p27), p53, and histone 3 trimethylated lysine 9 (H3K9me3). Senescence induction leads to the inhibition of self-renewal of tumor cells and blockage of tumor formation and growth. A signaling cascade was identified that acts downstream of Shp2 to counter senescence: Src, focal adhesion kinase, and Map kinase inhibit senescence by activating the expression of S-phase kinase-associated protein 2 (Skp2), Aurora kinase A (Aurka), and the Notch ligand Delta-like 1 (Dll1), which block p27 and p53. Remarkably, the expression of Shp2 and of selected target genes predicts human breast cancer outcome. We conclude that therapies, which rely on senescence induction by inhibiting Shp2 or controlling its target gene products, may be useful in blocking breast cancer.

Are we able to predict survival in ER-positive HER2-negative breast cancer? A comparison of web-based models

Background:

Several prognostic models have been proposed and demonstrated to be predictive of survival outcomes in breast cancer. In the present article, we assessed whether three of these models are comparable at an individual level.

Methods:

We used a large data set (n=965) of women with hormone receptor-positive and HER2-negative early breast cancer from the public data set of the METABRIC (Molecular Taxonomy of Breast Cancer International Consortium) study. We compared the overall performance of three validated web-based models: Adjuvant!, CancerMath.net and PREDICT, and we assessed concordance of these models in 10-year survival prediction.

Results:

Discrimination performances of the three calculators to predict 10-year survival were similar for the Adjuvant! Model, 0.74 (95% CI 0.71–0.77) for the Cancermath.net model and 0.72 (95% CI 0.69–0.75) for the PREDICT model). Calibration performances, assessed graphically, were satisfactory. Predictions were concordant and stable in the subgroup, with a predicted survival higher than 90% with a median score dispersion at 0.08 (range 0.06–0.10). Dispersion, however, reached 30% for the subgroups with a predicted survival between 10 and 50%.

Conclusion:

This study revealed that the three web-based predictors equally perform well at the population level, but exhibit a high degree of discordance in the intermediate and poor prognosis groups.

Clinicians' expectations for gene-driven cancer therapy

A new era of medicine is rapidly approaching, which will change not only pathological diagnosis but also medical decision-making. This paper raises the question of how well prepared doctors are to address the new issues that will soon confront them. The human genome has been completely sequenced and general understanding about cancer biology has increased enormously with understanding that unregulated gene function and complicated changes in signal pathways are related to uncontrolled cell growth. Thus, gene-driven therapy involving alterations to genes are recognized to present new therapy options. This advance will necessitate major changes to the decision-making aspect of physicians. This article focuses on defining the pertinent changes and addressing what they mean for practicing physicians.

Breast cancer therapy planning - a novel support concept for a sequential decision making problem

Breast cancer is the most common carcinosis with the largest number of mortalities in women. Its therapy comprises a wide spectrum of different treatment modalities a breast oncologist decides about for the individual patient case. These decisions happen according to medical guide lines, current scientific publications and experiences acquired in former cases. Clinical decision making therefore involves the time-consuming search for possible therapy options and their thorough testing for applicability to the current patient case.This research work addresses breast cancer therapy planning as a multi-criteria sequential decision making problem. The approach is based on a data model for patient cases with therapy descriptions and a mathematical notion for therapeutic relevance of medical information. This formulation allows for a novel decision support concept, which targets at eliminating observed weaknesses in clinical routine of breast cancer therapy planning.

The pyrido[b]indole MDM2 inhibitor SP-141 exerts potent therapeutic effects in breast cancer models

A requirement for Mouse Double Minute 2 (MDM2) oncogene activation has been suggested to be associated with cancer progression and metastasis, including breast cancer. To date, most MDM2 inhibitors have been designed to block the MDM2-p53-binding interphase, and have low or no efficacy against advanced breast cancer with mutant or deficient p53. Here we use a high-throughput screening and computer-aided, structure-based rational drug design, and identify a lead compound, SP-141, which can directly bind to MDM2, inhibit MDM2 expression and induce its autoubiquitination and proteasomal degradation. SP-141 has strong in vitro and in vivo antibreast cancer activity, with no apparent host toxicity. While further investigation is needed, our data indicate that SP-141 is a novel targeted therapeutic agent that may especially benefit patients with advanced disease.

Factors influencing Oncotype DX use in the management of early breast cancer: a single centre experience

BACKGROUND

Oncotype DX recurrence score is a multi-gene assay which quantifies the risk of distant recurrence in patients with hormone receptor-positive (HR+) early breast cancer (EBC) treated with tamoxifen, and predicts the magnitude of clinical benefit of adjuvant chemotherapy. This retrospective study examined factors that were associated with use of Oncotype DX assay at a tertiary care cancer centre in Ottawa, Canada.

METHODS

One hundred consecutive patients (pts) diagnosed with HR+, HER2/neu negative EBC (stage I-II), who underwent Oncotype DX testing (Test Group) between 1st April 2010, and 30th June 2011 were included in the study. A second cohort of 100 randomly selected patients with HR+, HER2/neu negative EBC diagnosed from the same time period who did not receive Oncotype DX testing were used as the control group (Control Group). Demographic and clinicopathologic data were obtained from review of charts. Logistic regression was performed to identify variables associated with Oncotype DX usage.

FINDINGS

Median age was 58 years (r: 26-77) in Test Group and 63 years (r: 30-81) in Control Group. Sixty-two patients in the Test Group had T1 tumours, compared with 71 in the Control Group. The median 10-year recurrence risks from Adjuvant! Online were 19% and 12% in the Test Group and Control Group, respectively. Factors significantly associated with the utilisation of Oncotype DX assay on multivariate analysis include age 50-64 (p=0.049), tumour size 10.1-20mm (p=0.008) and grade 2 histological grade (p=0.004).

INTERPRETATION

Usage of Oncotype DX assay is associated with several clinicopathological factors. These factors reflect the clinical uncertainty of benefit from chemotherapy in these subpopulations of patients and suggest how Oncotype DX assay could complement clinicopathological factors in helping clinicians on treatment selection.

Expression of androgen and estrogen signaling components and stem cell markers to predict cancer progression and cancer-specific survival in patients with metastatic prostate cancer

PURPOSE

Genes of androgen and estrogen signaling cells and stem cell-like cells play crucial roles in prostate cancer. This study aimed to predict clinical failure by identifying these prostate cancer-related genes.

EXPERIMENTAL DESIGN

We developed models to predict clinical failure using biopsy samples from a training set of 46 and an independent validation set of 30 patients with treatment-naïve prostate cancer with bone metastasis. Cancerous and stromal tissues were separately collected by laser-captured microdissection. We analyzed the association between clinical failure and mRNA expression of the following genes androgen receptor (AR) and its related genes (APP, FOX family, TRIM 36, Oct1, and ACSL 3), stem cell-like molecules (Klf4, c-Myc, Oct 3/4, and Sox2), estrogen receptor (ER), Her2, PSA, and CRP.

RESULTS

Logistic analyses to predict prostate-specific antigen (PSA) recurrence showed an area under the curve (AUC) of 1.0 in both sets for Sox2, Her2, and CRP expression in cancer cells, AR and ERα expression in stromal cells, and clinical parameters. We identified 10 prognostic factors for cancer-specific survival (CSS): Oct1, TRIM36, Sox2, and c-Myc expression in cancer cells; AR, Klf4, and ERα expression in stromal cells; and PSA, Gleason score, and extent of disease. On the basis of these factors, patients were divided into favorable-, intermediate-, and poor-risk groups according to the number of factors present. Five-year CSS rates for the 3 groups were 90%, 32%, and 12% in the training set and 75%, 48%, and 0% in the validation set, respectively.

CONCLUSIONS

Expression levels of androgen- and estrogen signaling components and stem cell markers are powerful prognostic tools.

Combined Wnt/β-catenin, Met, and CXCL12/CXCR4 signals characterize basal breast cancer and predict disease outcome

Prognosis for patients with estrogen-receptor (ER)-negative basal breast cancer is poor, and chemotherapy is currently the best therapeutic option. We have generated a compound-mutant mouse model combining the activation of β-catenin and HGF (Wnt-Met signaling), which produced rapidly growing basal mammary gland tumors. We identified the chemokine system CXCL12/CXCR4 as a crucial driver of Wnt-Met tumors, given that compound-mutant mice also deficient in the CXCR4 gene were tumor resistant. Wnt-Met activation rapidly expanded a population of cancer-propagating cells, in which the two signaling systems control different functions, self-renewal and differentiation. Molecular therapy targeting Wnt, Met, and CXCR4 in mice significantly delayed tumor development. The expression of a Wnt-Met 322 gene signature was found to be predictive of poor survival of human patients with ER-negative breast cancers. Thus, targeting CXCR4 and its upstream activators, Wnt and Met, might provide an efficient strategy for breast cancer treatment.

Genome variation and personalized cancer medicine

Genomic variation, through effects on gene structure and expression, plays an important role in understanding disease predisposition, biology and clinical response to therapy. Transforming this knowledge into clinically relevant information that tailors interventions to an individual's specific genetic, physical, social and environmental profile is challenging. To illustrate how research initiatives at preclinical phases of development are attempting to address clinically important issues in oncology, six clinical problems related to cancers of the colon, prostate, breast, pancreas and brain (medulloblastoma) as well as metastatic disease of different origins are described. A unifying theme across applications is that healthy individuals previously indistinguishable in regards to cancer risk and patients with cancer previously categorized as similar with regard to prognosis or drug response are being stratified into more refined subgroups with different clinical profiles. Effective matching of a broad range of tests with more tailored strategies for prevention and/or treatment will require well-designed clinical studies to evaluate benefits and costs.

Bench-to-bedside review: future novel diagnostics for sepsis - a systems biology approach

The early, accurate diagnosis and risk stratification of sepsis remains an important challenge in the critically ill. Since traditional biomarker strategies have not yielded a gold standard marker for sepsis, focus is shifting towards novel strategies that improve assessment capabilities. The combination of technological advancements and information generated through the human genome project positions systems biology at the forefront of biomarker discovery. While previously available, developments in the technologies focusing on DNA, gene expression, gene regulatory mechanisms, protein and metabolite discovery have made these tools more feasible to implement and less costly, and they have taken on an enhanced capacity such that they are ripe for utilization as tools to advance our knowledge and clinical research. Medicine is in a genome-level era that can leverage the assessment of thousands of molecular signals beyond simply measuring selected circulating proteins. Genomics is the study of the entire complement of genetic material of an individual. Epigenetics is the regulation of gene activity by reversible modifications of the DNA. Transcriptomics is the quantification of the relative levels of messenger RNA for a large number of genes in specific cells or tissues to measure differences in the expression levels of different genes, and the utilization of patterns of differential gene expression to characterize different biological states of a tissue. Proteomics is the large-scale study of proteins. Metabolomics is the study of the small molecule profiles that are the terminal downstream products of the genome and consists of the total complement of all low-molecular-weight molecules that cellular processes leave behind. Taken together, these individual fields of study may be linked during a systems biology approach. There remains a valuable opportunity to deploy these technologies further in human research. The techniques described in this paper not only have the potential to increase the spectrum of diagnostic and prognostic biomarkers in sepsis, but they may also enable the discovery of new disease pathways. This may in turn lead us to improved therapeutic targets. The objective of this paper is to provide an overview and basic framework for clinicians and clinical researchers to better understand the 'omics technologies' to enhance further use of these valuable tools.

Relevance of molecular characterization of circulating tumor cells in breast cancer in the era of targeted therapies

Development in circulating tumor cells (CTCs) technologies represents a valuable tool for the better understanding of tumor biology. The clinical relevance of CTCs as a prognostic factor is well established both in metastatic and early-stage breast cancer patients. The eradication or decrease of CTCs following treatment is associated with improved clinical outcomes. Because of the availability of novel cancer treatments that specifically target tumor cells underlying signaling pathways, molecular characterization of CTCs has strong potential to translate into personalized treatments. A handful of studies have explored relevant markers such as the estrogen and progesterone receptor, HER2 and EGF receptor. However, there is not a single validation of a molecular marker in CTCs that provides prognostic information or predicts response to cancer therapies. This review describes the latest results on the characterization of breast cancer CTCs with a focus on CTC biology and implications in clinical practice.

The impact of the Oncotype Dx breast cancer assay in clinical practice: a systematic review and meta-analysis

The impact of the Oncotype Dx (ODX) breast cancer assay on adjuvant chemotherapy (ACT) treatment decisions has been evaluated in many previous studies. However, it can be difficult to interpret the collective findings, which were conducted in diverse settings with limited sample sizes. We conducted a systematic review and meta-analysis to synthesize the results and provide insights about ODX utility. Studies, identified from PubMed, Embase, ASCO, and SABCS, were included if patients had ER+, node -, early-stage breast cancer, reported use of ODX to inform actual ACT decisions. Information was summarized and pooled according to: (1) distribution of ODX recurrence scores (RS), (2) impact of ODX on ACT recommendations, (3) impact of ODX on ACT use, and (4) proportion of patients following the treatment suggested by the ODX RS. A total of 23 studies met inclusion criteria. The distribution of RS categories was 48.8 % low, 39.0 % intermediate, and 12.2 % high (21 studies, 4,156 patients). ODX changed the clinical-pathological ACT recommendation in 33.4 % of patients (8 studies, 1,437 patients). In patients receiving ODX, receipt of ACT were: 28.2 % overall, 5.8 % low, 37.4 % intermediate, and 83.4 % high. Low RS patients were significantly more likely to follow the treatment suggested by ODX versus high RS patients RR: 1.07 (1.01–1.14) [corrected].The pooled results are consistent with most individual studies to date. The increased proportion of intermediate scores relative to original estimates may have implications for the clinical utility and cost impacts of testing. In addition, low versus high RS patients were significantly more likely to follow the ODX results, suggesting a tendency toward less aggressive treatment, despite a high ODX RS. Finally, there was a lack of studies on the impact of ODX on ACT use versus standard approaches, suggesting that additional studies are warranted.

The dyslipidemia-associated SNP on the APOA1/C3/A5 gene cluster predicts post-surgery poor outcome in Taiwanese breast cancer patients: a 10-year follow-up study

Background

Post-surgery therapies are given to early-stage breast cancer patients due to the possibility of residual micrometastasis, and optimized by clincopathological parameters such as tumor stage, and hormone receptor/lymph node status. However, current efficacy of post-surgery therapies is unsatisfactory, and may be varied according to unidentified patient genetic factors. Increases of breast cancer occurrence and recurrence have been associated with dyslipidemia, which can attribute to other known risk factors of breast cancer including obesity, diabetes and metabolic syndrome. Thus we reasoned that dyslipidemia-associated nucleotide polymorphisms (SNPs) on the APOA1/C3/A5 gene cluster may predict breast cancer risk and tumor progression.

Methods

We analyzed the distribution of 5 selected APOA1/C3/A5 SNPs in recruited Taiwanese breast cancer patients (n=223) and healthy controls (n=162). The association of SNP (APOA1 rs670) showing correlation with breast cancer with baseline and follow-up parameters was further examined.

Results

APOA1 rs670 A allele carriage was higher in breast cancer patients than controls (59.64% vs. 48.77%, p=0.038). The rs670 A allele carrying patients showed less favorable baseline phenotype with positive lymph nodes (G/A: OR=3.32, 95% CI=1.77-6.20, p<0.001; A/A: OR=2.58, 95% CI=1.05-6.32, p=0.039) and negative hormone receptor expression (A/A: OR=4.85, 95%CI=1.83-12.83, p=0.001) in comparison to G/G carriers. Moreover, rs670 A/A carrying patients had higher risks in both tumor recurrence (HR=3.12, 95% CI=1.29-7.56, p=0.012) and mortality (HR=4.36, 95% CI=1.52-12.47, p=0.006) than patients with no A alleles after adjustments for associated baseline parameters. Furthermore, the prognostic effect of rs670 A/A carriage was most evident in lymph node-negative patients, conferring to the highest risks of recurrence (HR=4.98, 95% CI=1.40-17.70, p=0.013) and mortality (HR=9.87, 95%CI=1.60-60.81, p=0.014) than patients with no A alleles.

Conclusions

APOA1 rs670 A/A carriage showed poor post-surgery prognosis in Taiwanese lymph node-negative breast cancer patients, whose prognosis were considered better and adjuvant treatment might be less stringent according to currently available assessment protocols. Our findings suggest that APOA1 rs670 indicate a post-surgery risk of breast cancer disease progression, and that carriers of this SNP may benefit from more advanced disease monitoring and therapy regimens than the current regular standards. Furthermore, control of lipid homeostasis might protect APOA1 rs670 minor allele carriers from breast cancer occurrence and progression.

The EndoPredict Gene-Expression Assay in Clinical Practice - Performance and Impact on Clinical Decisions

The validated EndoPredict assay is a novel tool to predict the risk of metastases of patients with estrogen receptor positive, HER2 negative breast cancer treated with endocrine therapy alone. It has been designed to integrate genomic and clinical information and includes clinico-pathological factors such as tumor size and nodal status. The test is feasible in a decentral setting in molecular pathology laboratories. In this project, we investigated the performance of this test in clinical practice, and performed a retrospective evaluation of its impact on treatment decisions in breast cancer. During one year, EndoPredict assays from 167 patients could be successfully performed. For retrospective evaluation of treatment decisions, a questionnaire was sent to the clinical partner. Regarding the molecular EP class, samples from 56 patients (33.5%) had a low-risk, whereas 111 patients (66.5%) showed a high-risk gene profile. After integration of the clinicopathological factors the combined clinical and molecular score (EPclin) resulted in a low-risk group of 77 patients (46.4%), while 89 (53.6%) had a high risk EPclin score. The EPclin-based estimated median 10-year-risk for metastases with endocrine therapy alone was 11% for the whole cohort. The median handling time averaged three days (range: 0 to 11 days), 59.3% of the tests could be performed in three or less than three days. Comparison of pre- and post-test therapy decisions showed a change of therapy in 37.7% of patients. 16 patients (12.3%) had a change to an additional chemotherapy while 25.4% of patients (n = 33) changed to an endocrine therapy alone. In 73 patients (56.2%) no change of therapy resulted. In 6.1% of patients (n = 8), the patients did not agree to the recommendation of the tumor board. Our results show that the EndoPredict assay could be routinely performed in decentral molecular pathology laboratories and the results markedly change treatment decisions.

The prognostic value of tumor budding in invasive breast cancer

We investigated the prognostic value of tumor budding in 160 cases of operable invasive ductal carcinoma, not otherwise specified (IDC-NOS). The number of buds was counted in H&E slides with a maximal invasive margin in a 0.950mm(2) field of vision (200×). According to a cut-off score selected by ROC analysis, the cohort was dichotomized into a low (0-7 budding foci, 107 cases, 66.9%) and a high-grade budding group (8 or more budding foci, 53 cases, 33.1%). The inter-observer test showed a good reproducibility with 0.717 as the К value. High-grade budding was significantly associated with the presence of lymphovascular invasion (LVI) (P=0.001), larger tumor size (P=0.014), and worse clinical outcome (P<0.001). By immunohistochemical staining, budded cells at the margin displayed epithelial mesenchymal transition (EMT)-like molecular phenotype and decreased proliferative activity. Survival analyses revealed that tumor budding (HR 4.275, P<0.001) together with tumor size (HR 2.468, P=0.002), node status (HR 2.362, P<0.001), and LVI status (HR 1.910, P=0.035) was the independent prognostic factor in IDC-NOS. In conclusion, tumor budding is a reproducible, significant, and independent histopathological prognostic factor in IDC-NOS.

Hormone receptor expression in breast cancer: postanalytical issues

Hormone receptor expression is a critical part of the pathological evaluation of breast cancer. Underpinning not only therapeutic decisions and prognosis, oestrogen receptor (ER) and progesterone receptor (PR) have been a consistent thread in the expanding knowledge of breast cancer. Accurate laboratory testing requires care and precision in preanalytical, analytical and postanalytical processes. In this report, postanalytical issues of pathologist interpretation of ER and PR status in breast cancer are discussed. Apart from key elements of the actual pathological assessment, it is important to realise that there are additional factors that can impact on sensitivity, specificity and dynamic range of hormone receptor expression as rendered on pathology. These include tumour characteristics and heterogeneity, biological changes of tumour progression and interacting molecules, all of which can influence the degree of hormone responsiveness in a particular individual with hormone receptor-positive breast cancer. There is a need to ensure participation in quality assurance programmes and slide exchanges, as well as to constantly keep abreast of emerging data on clinical trials and outcomes of hormone receptor-positive breast cancer.

Proteomics in studying cancer stem cell biology

Normal multipotent tissue stem cells (SCs) are the driving force behind tissue turnover and repair. The cancer stem cell theory holds that tumors also contain stem-like cells that drive tumor growth and metastasis formation. However, very little is known about the regulation of SC maintenance pathways in cancer and how these are affected by cancer-specific genetic alterations and by treatment. Proteomics is emerging as a powerful tool to identify the signaling complexes and pathways that control multi- and pluri-potency and SC differentiation. Here, the authors review the novel insights that these studies have provided and present a comprehensive strategy for the use of proteomics in studying cancer SC biology.

Epigenetics advancing personalized nanomedicine in cancer therapy

Personalized medicine aims to deliver the right drug to a right patient at the right time. It offers unique opportunities to integrate new technologies and concepts to disease prognosis, diagnosis and therapeutics. While selective personalized therapies are conceptually impressive, the majority of cancer therapies have dismal outcome. Such therapeutic failure could result from no response, drug resistance, disease relapse or severe side effect from improper drug delivery. Nanomedicine, the application of nanotechnology in medicine, has a potential to advance the identification of diagnostic and prognostic biomarkers and the delivery of right drug to disease sites. Epigenetic aberrations dynamically contribute to cancer pathogenesis. Given the individualized traits of epigenetic biomarkers, epigenetic considerations would significantly refine personalized nanomedicine. This review aims to dissect the interface of personalized medicine with nanomedicine and epigenetics. I will outline the progress and highlight challenges and areas that can be further explored perfecting the personalized health care.

Beyond PSA: the next generation of prostate cancer biomarkers

Since the introduction of serum prostate-specific antigen (PSA) screening 25 years ago, prostate cancer diagnosis and management have been guided by this biomarker. Yet, PSA has proven controversial as a screening assay owing to several inherent limitations. The next wave of prostate cancer biomarkers has emerged, introducing new assays in serum and urine that may supplement or, in time, replace PSA because of their higher cancer specificity. This expanding universe of biomarkers has been facilitated, in large part, by new genomic technologies that have enabled an unbiased look at cancer biology. Such efforts have produced several notable success stories that involve rapidly moving biomarkers from the bench to the clinic. However, biomarker research has centered on disease diagnostics, rather than prognosis and prediction, which would address disease management. The development of biomarkers to stratify risk of prostate cancer aggressiveness at the time of screening remains the greatest unmet clinical need in prostate cancer. We review the current state of prostate cancer biomarker research, including the PSA revolution, its impact on early cancer detection, the recent advances in biomarker discovery, and the future efforts that promise to improve clinical management of this disease.

Comparison of microarray platforms for measuring differential microRNA expression in paired normal/cancer colon tissues

Background

Microarray technology applied to microRNA (miRNA) profiling is a promising tool in many research fields; nevertheless, independent studies characterizing the same pathology have often reported poorly overlapping results. miRNA analysis methods have only recently been systematically compared but only in few cases using clinical samples.

Methodology/Principal Findings

We investigated the inter-platform reproducibility of four miRNA microarray platforms (Agilent, Exiqon, Illumina, and Miltenyi), comparing nine paired tumor/normal colon tissues. The most concordant and selected discordant miRNAs were further studied by quantitative RT-PCR. Globally, a poor overlap among differentially expressed miRNAs identified by each platform was found. Nevertheless, for eight miRNAs high agreement in differential expression among the four platforms and comparability to qRT-PCR was observed. Furthermore, most of the miRNA sets identified by each platform are coherently enriched in data from the other platforms and the great majority of colon cancer associated miRNA sets derived from the literature were validated in our data, independently from the platform. Computational integration of miRNA and gene expression profiles suggested that anti-correlated predicted target genes of differentially expressed miRNAs are commonly enriched in cancer-related pathways and in genes involved in glycolysis and nutrient transport.

Conclusions

Technical and analytical challenges in measuring miRNAs still remain and further research is required in order to increase consistency between different microarray-based methodologies. However, a better inter-platform agreement was found by looking at miRNA sets instead of single miRNAs and through a miRNAs – gene expression integration approach.