Development of the 21-gene assay and its application in clinical practice and clinical trials

Predictive Gene Signature in MAGE-A3 Antigen-Specific Cancer Immunotherapy

Purpose

To detect a pretreatment gene expression signature (GS) predictive of response to MAGE-A3 immunotherapeutic in patients with metastatic melanoma and to investigate its applicability in a different cancer setting (adjuvant therapy of resected early-stage non–small-cell lung cancer [NSCLC]).

Patients and Methods

Patients were participants in two phase II studies of the recombinant MAGE-A3 antigen combined with an immunostimulant (AS15 or AS02B). mRNA from melanoma biopsies was analyzed by microarray analysis and quantitative polymerase chain reaction. These results were used to identify and cross-validate the GS, which was then applied to the NSCLC data.

Results

In the patients with melanoma, 84 genes were identified whose expression was potentially associated with clinical benefit. This effect was strongest when the immunostimulant AS15 was included in the immunotherapy (hazard ratio [HR] for overall survival, 0.37; 95% CI, 0.13 to 1.05; P = .06) and was less strong with the other immunostimulant AS02B (HR, 0.84; 95% CI, 0.36 to 1.97; P = .70). The same GS was then used to predict the outcome for patients with resected NSCLC treated with MAGE-A3 plus AS02B; actively treated GS-positive patients showed a favorable disease-free interval compared with placebo-treated GS-positive patients (HR, 0.42; 95% CI, 0.17 to 1.03; P = .06), whereas among GS-negative patients, no such difference was found (HR, 1.17; 95% CI, 0.59 to 2.31; P = .65). The genes identified were mainly immune related, involving interferon gamma pathways and specific chemokines, suggesting that their pretreatment expression influences the tumor's immune microenvironment and the patient's clinical response.

Conclusion

An 84-gene GS associated with clinical response for MAGE-A3 immunotherapeutic was identified in metastatic melanoma and confirmed in resected NSCLC.

Ki-67 is a prognostic parameter in breast cancer patients: results of a large population-based cohort of a cancer registry

The proliferation marker Ki-67 is one of the most controversially discussed parameters for treatment decisions in breast cancer patients. The purpose of this study was to evaluate the routine use and value of Ki-67 as a prognostic marker, and to analyze the associations between Ki-67 and common histopathological parameters in the routine clinical setting. Data from the clinical cancer registry Regensburg (Bavaria, Germany) were analyzed. Within the total data pool of 4,692 female patients, who had been diagnosed between 2005 and 2011, in 3,658 cases Ki-67 was routinely determined. Thus, a total of 3,658 patients with invasive breast cancer were included in the present study and used for statistical analysis. Ki-67 expression was associated with the common histopathological parameters. The strongest correlation was found between grading and Ki-67 (P < 0.001). In terms of survival analyses, Ki-67 was categorized into five categories (reference category Ki-67 ≤15 %) due to a nonlinear relationship to overall survival (OS). In multivariable analysis, Ki-67 was an independent prognostic parameter both for disease-free survival (DFS) (Ki-67 > 45 %, HR = 1.96, P = 0.001) as well as for OS (Ki-67: 26-35 %, HR = 1.71, P = 0.017; Ki-67: 36-45 %, HR = 2.05, P = 0.011; Ki-67 > 45 %, HR = 2.06, P = 0.002) independent of common clinical and histopathological factors. The 5-year DFS (OS) rate was 86.7 % (89.3 %) in patients with a Ki-67 value ≤15 % compared to 75.8 % (82.8 %) in patients with a Ki-67 value >45 %. Based on the data from a large cohort of a clinical cancer registry, it was demonstrated that Ki-67 is frequently determined in routine clinical work. Ki-67 expression is associated with common histopathological parameters, but is an additional independent prognostic parameter for DFS and OS in breast cancer patients. Future work should focus on standardization of Ki-67 assessment and specification of its role in treatment decisions.

Is oncotype DX recurrence score (RS) of prognostic value once HER2-positive and. low-ER expression patients are removed?

Oncotype DX has been criticized for not providing significantly more prognostic information than histopathologic analysis. Oncotype DX was validated in cohorts that included poor prognostic factors (HER2-positive, low-estrogen receptor [ER] expression), raising the question: if patients with known high recurrence rates are excluded, is the Recurrence Score (RS) still valid? Our purpose was to determine if RS can be predicted with readily available measures. One hundred and twenty samples from August 2006 to November 2010 that underwent Oncotype DX testing were analyzed. Data included RS, ER, progesterone receptor (PR), HER2, and Ki67 status by immunohistochemistry (IHC). IHC data were used to create two linear regression models to predict RS. SAS's JMP-7 was used for statistical analysis. When comparing Oncotype DX- and IHC-derived ER and PR values, there were 21 discordant samples. The linear regression model PRS-F created with IHC data (ER, PR, HER2, Ki67) from all samples (n = 120) had an adjusted R(2) = 0.60 indicating a good model for predicting RS. The PRS-R model was built without low-ER and HER2-positive samples (n = 110). It had an adjusted R(2) = 0.38 indicating poor prediction of RS. Oncotype DX data showed good concordance with IHC for ER- and PR-expression in this cohort. Low-ER samples had high RS. After removing low-ER and HER2-positives, calculating RS with PRS-R from remaining data showed poor predictive power for RS (adjusted R(2) = 0.38). This result questions whether RS is prognostic in this subgroup (who would most benefit from further clarification of recurrence risk) and independent of pathology, or is simply producing random RS values. Data bases available to Genomic Health can resolve this issue.

Lactate dehydrogenase B: a metabolic marker of response to neoadjuvant chemotherapy in breast cancer

PURPOSE

Although breast cancers are known to be molecularly heterogeneous, their metabolic phenotype is less well-understood and may predict response to chemotherapy. This study aimed to evaluate metabolic genes as individual predictive biomarkers in breast cancer.

EXPERIMENTAL DESIGN

mRNA microarray data from breast cancer cell lines were used to identify bimodal genes-those with highest potential for robust high/low classification in clinical assays. Metabolic function was evaluated in vitro for the highest scoring metabolic gene, lactate dehydrogenase B (LDHB). Its expression was associated with neoadjuvant chemotherapy response and relapse within clinical and PAM50-derived subtypes.

RESULTS

LDHB was highly expressed in cell lines with glycolytic, basal-like phenotypes. Stable knockdown of LDHB in cell lines reduced glycolytic dependence, linking LDHB expression directly to metabolic function. Using patient datasets, LDHB was highly expressed in basal-like cancers and could predict basal-like subtype within clinical groups [OR = 21 for hormone receptor (HR)-positive/HER2-negative; OR = 10 for triple-negative]. Furthermore, high LDHB predicted pathologic complete response (pCR) to neoadjuvant chemotherapy for both HR-positive/HER2-negative (OR = 4.1, P < 0.001) and triple-negative (OR = 3.0, P = 0.003) cancers. For triple-negative tumors without pCR, high LDHB posttreatment also identified proliferative tumors with increased risk of recurrence (HR = 2.2, P = 0.006).

CONCLUSIONS

Expression of LDHB predicted response to neoadjuvant chemotherapy within clinical subtypes independently of standard prognostic markers and PAM50 subtyping. These observations support prospective clinical evaluation of LDHB as a predictive marker of response for patients with breast cancer receiving neoadjuvant chemotherapy.

Potential impact of the 70-gene signature in the choice of adjuvant systemic treatment for ER positive, HER2 negative tumors: a single institution experience

PURPOSE

We investigated in a single institution series of 124 women with operable breast cancer whether tumor clinicopathological features could predict the 70-gene signature (Mammaprint, MP) results, and whether MP results could help to make decisions for the use of chemotherapy (CT) in patients (pts) with ER positive breast cancer beyond recommendations of international guidelines.

RESULTS

Among the 68 ER/PgR positive, HER2 negative tumors, Ki-67 ≥ 20% was the only significant predictor of a high risk-MP among standard clinicopathological features. In candidates for endocrine therapy with undetermined benefit from CT according to international guidelines, MP results would have led to different treatment decisions in 13/46 (28%) and in 20/68 (29%) pts according to NCCN and St. Gallen recommendations, respectively.

CONCLUSIONS

Ki-67 independently predicted high risk-MP in ER/PgR positive, HER2 negative tumors. MP results would have led to discordant treatment recommendations in about 30% of cases, generally increasing indication rate for CT. The results of large randomized trials are warranted in order to understand whether we should rely on multigene assays rather than on standard clinicopathological features for treatment decisions.

Prediction of cancer outcome using DNA microarray technology: past, present and future

BACKGROUND

The use of DNA microarray technology to predict cancer outcome already has a history of almost a decade. Although many breakthroughs have been made, the promise of individualized therapy is still not fulfilled. In addition, new technologies are emerging that also show promise in outcome prediction of cancer patients.

OBJECTIVE

The impact of DNA microarray and other 'omics' technologies on the outcome prediction of cancer patients was investigated. Whether integration of omics data results in better predictions was also examined.

METHODS

DNA microarray technology was focused on as a starting point because this technology is considered to be the most mature technology from all omics technologies. Next, emerging technologies that may accomplish the same goals but have been less extensively studied are described.

CONCLUSION

Besides DNA microarray technology, other omics technologies have shown promise in predicting the cancer outcome or have potential to replace microarray technology in the near future. Moreover, it is shown that integration of multiple omics data can result in better predictions of cancer outcome; but, owing to the lack of comprehensive studies, validation studies are required to verify which omics has the most information and whether a combination of multiple omics data improves predictive performance.

Conveying genomic recurrence risk estimates to patients with early-stage breast cancer: oncologist perspectives

OBJECTIVE

The development and increased use of genomic profiling has led to refinement of breast cancer treatment. This study sought to examine medical and surgical oncologists' perceptions of factors related to the translation and integration of Oncotype DX® (Genomic Health, Inc., Redwood City, CA, USA) into routine clinical care.

METHODS

Twenty oncologists (10 medical and 10 surgical oncologists) participated in qualitative interviews. Questions centered on the following themes: oncologists' perceptions about the clinical utility of testing, the impact of patient preferences on the decision to test and use results to inform treatment decisions, methods of communicating risk associated with test results to patients, and benefits of and barriers to incorporating testing into multidisciplinary care settings.

RESULTS

Oncologists found Oncotype DX test results useful in their practice but had concerns as well. These included that some oncologists either used testing inappropriately or placed undue emphasis on the results at the expense of other clinical information. The use of intermediate test results, which have less clear clinical implications, incorporating results with patient treatment preferences, and the use of testing in multidisciplinary teams were noted as specific challenges.

CONCLUSION

Oncologists noted several benefits of testing and also many challenges, despite wide dissemination and common use. Education for health providers should include specific training in how to interpret and communicate the uncertainty inherent in genomic tests while integrating patient preferences to inform treatment decision making.

Risk assessment, disease prevention and personalised treatments in breast cancer: is clinically qualified integrative approach in the horizon?

Breast cancer is a multifactorial disease. A spectrum of internal and external factors contributes to the disease promotion such as a genetic predisposition, chronic inflammatory processes, exposure to toxic compounds, abundant stress factors, a shift-worker job, etc. The cumulative effects lead to high incidence of breast cancer in populations worldwide. Breast cancer in the USA is currently registered with the highest incidence rates amongst all cancer related patient cohorts. Currently applied diagnostic approaches are frequently unable to recognise early stages in tumour development that impairs individual outcomes. Early diagnosis has been demonstrated to be highly beneficial for significantly enhanced therapy efficacy and possibly full recovery. Actual paper shows that the elaboration of an integrative diagnostic approach combining several levels of examinations creates a robust platform for the reliable risk assessment, targeted preventive measures and more effective treatments tailored to the person in the overall task of breast cancer management. The levels of examinations are proposed, and innovative technological approaches are described in the paper. The absolute necessity to create individual patient profiles and extended medical records is justified for the utilising by routine medical services. Expert recommendations are provided to promote further developments in the field.

A new genome-driven integrated classification of breast cancer and its implications

Breast cancer is a group of heterogeneous diseases that show substantial variation in their molecular and clinical characteristics. This heterogeneity poses significant challenges not only in breast cancer management, but also in studying the biology of the disease. Recently, rapid progress has been made in understanding the genomic diversity of breast cancer. These advances led to the characterisation of a new genome-driven integrated classification of breast cancer, which substantially refines the existing classification systems currently used. The novel classification integrates molecular information on the genomic and transcriptomic landscapes of breast cancer to define 10 integrative clusters, each associated with distinct clinical outcomes and providing new insights into the underlying biology and potential molecular drivers. These findings have profound implications both for the individualisation of treatment approaches, bringing us a step closer to the realisation of personalised cancer management in breast cancer, but also provide a new framework for studying the underlying biology of each novel subtype.

Genomic grade adds prognostic value in invasive lobular carcinoma

BACKGROUND

The prognostic value of histologic grade (HG) in invasive lobular carcinoma (ILC) remains uncertain, and most ILC tumors are graded as HG2. Genomic grade (GG) is a 97-gene signature that improves the prognostic value of HG. This study evaluates whether GG may overcome the limitations of HG in ILC.

METHODS

Gene expression data were generated from frozen tumor samples, and GG calculated according to the expression of 97 genes. The prognostic value of GG was assessed in a stratified Cox regression model for invasive disease-free survival (IDFS) and overall survival (OS).

RESULTS

A total of 166 patients were classified by GG. HG classified 33 (20%) tumors as HG1, 120 (73%) as HG2 and 12 (7%) as HG3. GG classified 106 (64%) tumors as GG low (GG1), 29 (17%) as GG high (GG3) and 31 (19%) as equivocal (cases not classified as GG1 or GG3). The median follow-up time was 6.5 years. In multivariate analyses, GG was associated with IDFS [HR(GG3 vs GG1) 5.6 (2.1-15.3); P < 0.001] and OS [HR(GG3 vs GG1) 7.2, 95% CI (1.6-32.2); P = 0.01].

CONCLUSIONS

GG outperformed HG in ILC and added prognostic value to classic clinicopathologic variables, including nodal status.

Comparative clinical benefits of systemic adjuvant therapy for paradigm solid tumors

Adjuvant therapy employing cytotoxic chemotherapy, molecularly targeted agents, immunologic, and hormonal agents has shown a significant impact upon a variety of solid tumors. The principles that guide adjuvant therapy differ among various tumor types and specific modalities, but generally indicate a greater impact of therapy in the postsurgical setting of micrometastatic disease, for which adjuvant therapy is commonly pursued, vs. the setting of gross unresectable disease. This review of adjuvant therapies in current use for five major solid tumors highlights the rationale for current effective adjuvant therapy, and draws comparisons between the adjuvant regimens that have found application in solid tumors.

Gene expression signatures that predict outcome of tamoxifen-treated estrogen receptor-positive, high-risk, primary breast cancer patients: a DBCG study

Background

Tamoxifen significantly improves outcome for estrogen receptor-positive (ER+) breast cancer, but the 15-year recurrence rate remains 30%. The aim of this study was to identify gene profiles that accurately predicted the outcome of ER+ breast cancer patients who received adjuvant Tamoxifen mono-therapy.

Methodology/Principal Findings

Post-menopausal breast cancer patients diagnosed no later than 2002, being ER+ as defined by >1% IHC staining and having a frozen tumor sample with >50% tumor content were included. Tumor samples from 108 patients treated with adjuvant Tamoxifen were analyzed for the expression of 59 genes using quantitative-PCR. End-point was clinically verified recurrence to distant organs or ipsilateral breast. Gene profiles were identified using a model building procedure based on conditional logistic regression and leave-one-out cross-validation, followed by a non-parametric bootstrap (1000x re-sampling). The optimal profiles were further examined in 5 previously-reported datasets containing similar patient populations that were either treated with Tamoxifen or left untreated (n = 623). Three gene signatures were identified, the strongest being a 2-gene combination of BCL2-CDKN1A, exhibiting an accuracy of 75% for prediction of outcome. Independent examination using 4 previously-reported microarray datasets of Tamoxifen-treated patient samples (n = 503) confirmed the potential of BCL2-CDKN1A. The predictive value was further determined by comparing the ability of the genes to predict recurrence in an additional, previously-published, cohort consisting of Tamoxifen-treated (n = 58, p = 0.015) and untreated patients (n = 62, p = 0.25).

Conclusions/Significance

A novel gene expression signature predictive of outcome of Tamoxifen-treated patients was identified. The validation suggests that BCL2-CDKN1A exhibit promising predictive potential.

Engaging in health behaviors to lower risk for breast cancer recurrence

PURPOSE

While post-treatment breast cancer survivors face up to twice the cancer risk of the general population, modifiable health behaviors may somewhat reduce this risk. We sought to better understand health behaviors that early stage breast cancer survivors engage in to reduce recurrence risk.

METHODS

Data came from a cross-sectional multi-site survey of 186 early-stage breast cancer survivors who received genomic testing for breast cancer recurrence risk (Oncotype DX) during their clinical care. Study outcomes were meeting health behavior recommendations (daily fruit and vegetable intake, regular physical activity, and having a healthy body mass index (BMI)).

RESULTS

Approximately three-quarters of survivors we surveyed believed the 3 behaviors might reduce their cancer risk but many did not engage in these behaviors for this purpose: 62% for BMI, 36% for fruit and vegetable consumption, and 37% for physical activity. Survivors with higher recurrence risk, as indicated by their genomic test results, were no more likely to meet any of the three health behavior recommendations. Adherence to health behavior recommendations was higher for women who were white, college-educated, and had higher incomes.

CONCLUSIONS

Many nonadherent breast cancer survivors wish to use these behavioral strategies to reduce their risk for recurrence, suggesting an important opportunity for intervention. Improving BMI, which has the largest association with cancer risk, is an especially promising target.

Transcriptional shift identifies a set of genes driving breast cancer chemoresistance

Background

Distant recurrences after antineoplastic treatment remain a serious problem for breast cancer clinical management, which threats patients’ life. Systemic therapy is administered to eradicate cancer cells from the organism, both at the site of the primary tumor and at any other potential location. Despite this intervention, a significant proportion of breast cancer patients relapse even many years after their primary tumor has been successfully treated according to current clinical standards, evidencing the existence of a chemoresistant cell subpopulation originating from the primary tumor.

Methods/Findings

To identify key molecules and signaling pathways which drive breast cancer chemoresistance we performed gene expression analysis before and after anthracycline and taxane-based chemotherapy and compared the results between different histopathological response groups (good-, mid- and bad-response), established according to the Miller & Payne grading system. Two cohorts of 33 and 73 breast cancer patients receiving neoadjuvant chemotherapy were recruited for whole-genome expression analysis and validation assay, respectively. Identified genes were subjected to a bioinformatic analysis in order to ascertain the molecular function of the proteins they encode and the signaling in which they participate. High throughput technologies identified 65 gene sequences which were over-expressed in all groups (P ≤ 0·05 Bonferroni test). Notably we found that, after chemotherapy, a significant proportion of these genes were over-expressed in the good responders group, making their tumors indistinguishable from those of the bad responders in their expression profile (P ≤ 0.05 Benjamini-Hochgerg`s method).

Conclusions

These data identify a set of key molecular pathways selectively up-regulated in post-chemotherapy cancer cells, which may become appropriate targets for the development of future directed therapies against breast cancer.

Competing risks in low-risk breast cancer

In recent years a growing amount of data on prognostic features of breast cancer has allowed for identification of tumors with a very low risk of recurrence. Markers used to predict the risk of distant spread include classic clinicopathologic features as well as newer tumor gene signatures, which have been validated and are being used in cohorts of patients with breast cancer patients who have low-risk disease. However, the definition of "low-risk" breast cancer requires consideration of patient-related factors such as comorbidities and age in addition to tumor characteristics, as high competing risks for mortality might be more important than cancer recurrence from a patient's point of view. In addition, identification of low-risk breast cancer cohorts is only valuable if treatment decisions are based on this information. Therefore, the magnitude of any treatment benefit in low-risk disease needs to be quantified in a comprehensible way. However, treatment benefit in low-risk situations is hard to quantify, may vary over time, and needs to be compared to individual risks for side effects. Decision models considering tumor and patient characteristics as well as predictive markers for treatment efficacy and toxicity will be needed. Tools such as Adjuvant! Online ultimately need to include information from gene signatures in order to reliably recommend specific treatment options for patients with breast cancer patients who have low-risk disease.

Systematic evaluation of medium-throughput mRNA abundance platforms

Profiling of mRNA abundances with high-throughput platforms such as microarrays and RNA-seq has become an important tool in both basic and biomedical research. However, these platforms remain prone to systematic errors and have challenges in clinical and industrial applications. As a result, it is standard practice to validate a subset of key results using alternate technologies. Similarly, clinical and industrial applications typically involve transitions from a high-throughput discovery platform to medium-throughput validation ones. These medium-throughput validation platforms have high technical reproducibility and reduced sample input needs, and low sensitivity to sample quality (e.g., for processing FFPE specimens). Unfortunately, while medium-throughput platforms have proliferated, there are no comprehensive comparisons of them. Here we fill that gap by comparing two key medium-throughput platforms--NanoString's nCounter Analysis System and ABI's OpenArray System--to gold-standard quantitative real-time RT-PCR. We quantified 38 genes and positive and negative controls in 165 samples. Signal:noise ratios, correlations, dynamic range, and detection accuracy were compared across platforms. All three measurement technologies showed good concordance, but with divergent price/time/sensitivity trade-offs. This study provides the first detailed comparison of medium-throughput RNA quantification platforms and provides a template and a standard data set for the evaluation of additional technologies.

Tumor suppressor SCUBE2 inhibits breast-cancer cell migration and invasion through the reversal of epithelial-mesenchymal transition

SCUBE2 (signal peptide-CUB-EGF domain-containing protein 2) belongs to a secreted and membrane-associated multi-domain SCUBE protein family. We previously demonstrated that SCUBE2 was a novel breast-tumor suppressor and could be a useful prognostic marker. However, the role of SCUBE2 in breast-cancer cell migration and invasion and how it is regulated during the epithelial-mesenchymal transition (EMT) remain undefined. In this study, we showed that ectopic SCUBE2 overexpression could enhance the formation of E-cadherin-containing adherens junctions by β-catenin/SOX-mediated induction of forkhead box A1 (a positive regulator of E-cadherin) and upregulation of E-cadherin, which in turn led to epithelial transition and inhibited migration and invasion of aggressive MDA-MB-231 breast-carcinoma cells. SCUBE2 expression was repressed together with that of E-cadherin in TGF-β-induced EMT; direct expression of SCUBE2 alone was sufficient to inhibit the TGF-β-induced EMT. Furthermore, quantitative DNA methylation, methylation-specific PCR, and chromatin immunoprecipitation analyses revealed that SCUBE2 expression was inactivated by DNA hypermethylation at the CpG islands by recruiting and binding DNA methyltransferase 1 during TGF-β-induced EMT. Together, our results suggest that SCUBE2 plays a key role in suppressing breast-carcinoma cell mobility and invasiveness by increasing the formation of the epithelial E-cadherin-containing adherens junctions to promote epithelial differentiation and drive the reversal of EMT.

14-3-3ζ as a predictor of early time to recurrence and distant metastasis in hormone receptor-positive and -negative breast cancers

The 14-3-3ζ gene, on 8q22, is often amplified in breast cancer and encodes a survival factor that interacts with and stabilizes many key signaling proteins. We examined the relationship between the expression of 14-3-3ζ, estrogen receptor α (ERα), and other parameters ( tumor size, grade, nodal status, progesterone receptor, HER2, EGFR, and p53) in matched primary and recurrence tumor tissue and how these factors impact time to recurrence, properties of the recurred tumors, and site of metastasis. In this cohort of over 100 patients, median time to recurrence was 3 years (range 1-17 years). Our analyses of primary tumor microarray cores revealed that 14-3-3ζ status was significantly correlated with tumor grade, size, and ERα. Women with 14-3-3ζ-positive and ERα-negative tumors had the earliest time to recurrence (median 1 yr, p < 0.001, hazard ratio 2.89), while median time to recurrence was 7 years for 14-3-3ζ-negative and ER-positive tumors. Of recurred tumors, 70-75 % were positive for 14-3-3ζ, up from the 45 % positivity of primary tumors. High expression of 14-3-3ζ also correlated with site of recurrence and showed a propensity for distant metastases to lung and chest wall. Multifactor correlation regression analysis revealed 14-3-3ζ to be a non-redundant, independent variable that adds clinical strength in predicting risk for early recurrence in ER-positive and -negative breast cancers, providing information beyond that of all other clinical pathological features examined. Thus, high expression of 14-3-3ζ in the primary tumor was significantly associated with earlier time to recurrence and with distant metastasis. Furthermore, even when the primary breast cancers were negative-low for 14-3-3ζ, the majority acquired increased expression in the recurrence. The findings underscore the detrimental role played by 14-3-3ζ in tumor aggressiveness and suggest that reducing its expression or interfering with its actions might substantially improve the clinical outcome for breast cancer patients.

Validation of ovarian cancer gene expression signatures for survival and subtype in formalin fixed paraffin embedded tissues

INTRODUCTION

Gene expression signatures have been identified for epithelial ovarian cancer survival (TCGA) and intrinsic subtypes (Tothill et al.). One obstacle to clinical translation is that these signatures were developed using frozen tissue, whereas usually only formalin-fixed, paraffin embedded (FFPE) tissue is available. The aim of this study was to determine if gene expression signatures can be translated to fixed archival tissues.

METHODS

RNA extracted from FFPE sections from 240 primary ovarian cancers was analyzed by DASL on Illumina BeadChip arrays. Concordance of expression at the individual gene level was assessed by comparing array data from the same cancers (30 frozen samples analyzed on Affymetrix arrays versus FFPE DASL).

RESULTS

The correlation between FFPE and frozen survival signature estimates was 0.774. The TCGA signature using DASL was predictive of survival in 106 advanced stage high grade serous ovarian cancers (median survival 33 versus 60 months, estimated hazard ratio for death 2.30, P=0.0007). Similar to Tothill, we found using DASL that most high grade serous ovarian cancers (102/110, 93%) were assigned to subtypes 1, 2, 4 and 5, whereas most endometrioid, clear cell, mucinous and low grade serous cases (39/57, 68%) were assigned to subtypes 3 and 6 (P<10e-15).

CONCLUSIONS

Although individual probe estimates of microarrays may be weakly correlated between FFPE and frozen samples, combinations of probes have robust ability to predict survival and subtype. This suggests that it may be possible to use these signatures for prognostic and predictive purposes as we seek to individualize the treatment of ovarian cancer.

Clinical and multiple gene expression variables in survival analysis of breast cancer: analysis with the hypertabastic survival model

BACKGROUND

We explore the benefits of applying a new proportional hazard model to analyze survival of breast cancer patients. As a parametric model, the hypertabastic survival model offers a closer fit to experimental data than Cox regression, and furthermore provides explicit survival and hazard functions which can be used as additional tools in the survival analysis. In addition, one of our main concerns is utilization of multiple gene expression variables. Our analysis treats the important issue of interaction of different gene signatures in the survival analysis.

METHODS

The hypertabastic proportional hazards model was applied in survival analysis of breast cancer patients. This model was compared, using statistical measures of goodness of fit, with models based on the semi-parametric Cox proportional hazards model and the parametric log-logistic and Weibull models. The explicit functions for hazard and survival were then used to analyze the dynamic behavior of hazard and survival functions.

RESULTS

The hypertabastic model provided the best fit among all the models considered. Use of multiple gene expression variables also provided a considerable improvement in the goodness of fit of the model, as compared to use of only one. By utilizing the explicit survival and hazard functions provided by the model, we were able to determine the magnitude of the maximum rate of increase in hazard, and the maximum rate of decrease in survival, as well as the times when these occurred. We explore the influence of each gene expression variable on these extrema. Furthermore, in the cases of continuous gene expression variables, represented by a measure of correlation, we were able to investigate the dynamics with respect to changes in gene expression.

CONCLUSIONS

We observed that use of three different gene signatures in the model provided a greater combined effect and allowed us to assess the relative importance of each in determination of outcome in this data set. These results point to the potential to combine gene signatures to a greater effect in cases where each gene signature represents some distinct aspect of the cancer biology. Furthermore we conclude that the hypertabastic survival models can be an effective survival analysis tool for breast cancer patients.

Cutoff Finder: a comprehensive and straightforward Web application enabling rapid biomarker cutoff optimization

Gene or protein expression data are usually represented by metric or at least ordinal variables. In order to translate a continuous variable into a clinical decision, it is necessary to determine a cutoff point and to stratify patients into two groups each requiring a different kind of treatment. Currently, there is no standard method or standard software for biomarker cutoff determination. Therefore, we developed Cutoff Finder, a bundle of optimization and visualization methods for cutoff determination that is accessible online. While one of the methods for cutoff optimization is based solely on the distribution of the marker under investigation, other methods optimize the correlation of the dichotomization with respect to an outcome or survival variable. We illustrate the functionality of Cutoff Finder by the analysis of the gene expression of estrogen receptor (ER) and progesterone receptor (PgR) in breast cancer tissues. This distribution of these important markers is analyzed and correlated with immunohistologically determined ER status and distant metastasis free survival. Cutoff Finder is expected to fill a relevant gap in the available biometric software repertoire and will enable faster optimization of new diagnostic biomarkers. The tool can be accessed at http://molpath.charite.de/cutoff.

A systematic evaluation of multi-gene predictors for the pathological response of breast cancer patients to chemotherapy

Previous studies have reported conflicting assessments of the ability of cell line-derived multi-gene predictors (MGPs) to forecast patient clinical outcomes in cancer patients, thereby warranting an investigation into their suitability for this task. Here, 42 breast cancer cell lines were evaluated by chemoresponse tests after treatment with either TFAC or FEC, two widely used standard combination chemotherapies for breast cancer. We used two different training cell line sets and two independent prediction methods, superPC and COXEN, to develop cell line-based MGPs, which were then validated in five patient cohorts treated with these chemotherapies. This evaluation yielded high prediction performances by these MGPs, regardless of the training set, chemotherapy, or prediction method. The MGPs were also able to predict patient clinical outcomes for the subgroup of estrogen receptor (ER)-negative patients, which has proven difficult in the past. These results demonstrated a potential of using an in vitro-based chemoresponse data as a model system in creating MGPs for stratifying patients’ therapeutic responses. Clinical utility and applications of these MGPs will need to be carefully examined with relevant clinical outcome measurements and constraints in practical use.

Concordance among gene expression-based predictors for ER-positive breast cancer treated with adjuvant tamoxifen

BACKGROUND

ER-positive (ER+) breast cancer includes all of the intrinsic molecular subtypes, although the luminal A and B subtypes predominate. In this study, we evaluated the ability of six clinically relevant genomic signatures to predict relapse in patients with ER+ tumors treated with adjuvant tamoxifen only.

METHODS

Four microarray datasets were combined and research-based versions of PAM50 intrinsic subtyping and risk of relapse (PAM50-ROR) score, 21-gene recurrence score (OncotypeDX), Mammaprint, Rotterdam 76 gene, index of sensitivity to endocrine therapy (SET) and an estrogen-induced gene set were evaluated. Distant relapse-free survival (DRFS) was estimated by Kaplan-Meier and log-rank tests, and multivariable analyses were done using Cox regression analysis. Harrell's C-index was also used to estimate performance.

RESULTS

All signatures were prognostic in patients with ER+ node-negative tumors, whereas most were prognostic in ER+ node-positive disease. Among the signatures evaluated, PAM50-ROR, OncotypeDX, Mammaprint and SET were consistently found to be independent predictors of relapse. A combination of all signatures significantly increased the performance prediction. Importantly, low-risk tumors (>90% DRFS at 8.5 years) were identified by the majority of signatures only within node-negative disease, and these tumors were mostly luminal A (78%-100%).

CONCLUSIONS

Most established genomic signatures were successful in outcome predictions in ER+ breast cancer and provided statistically independent information. From a clinical perspective, multiple signatures combined together most accurately predicted outcome, but a common finding was that each signature identified a subset of luminal A patients with node-negative disease who might be considered suitable candidates for adjuvant endocrine therapy alone.

Pathway index models for construction of patient-specific risk profiles

Statistical methods for variable selection, prediction, and classification have proven extremely useful in moving personalized genomics medicine forward, in particular, leading to a number of genomic-based assays now in clinical use for predicting cancer recurrence. Although invaluable in individual cases, the information provided by these assays is limited. Most often, a patient is classified into one of very few groups (e.g., recur or not), limiting the potential for truly personalized treatment. Furthermore, although these assays provide information on which individuals are at most risk (e.g., those for which recurrence is predicted), they provide no information on the aberrant biological pathways that give rise to the increased risk. We have developed an approach to address these limitations. The approach models a time-to-event outcome as a function of known biological pathways, identifies important genomic aberrations, and provides pathway-based patient-specific assessments of risk. As we demonstrate in a study of ovarian cancer from The Cancer Genome Atlas project, the patient-specific risk profiles are powerful and efficient characterizations useful in addressing a number of questions related to identifying informative patient subtypes and predicting survival.

Publication of tumor marker research results: the necessity for complete and transparent reporting

Clinical management decisions for patients with cancer are increasingly being guided by prognostic and predictive markers. Use of these markers should be based on a sufficiently comprehensive body of unbiased evidence to establish that benefits to patients outweigh harms and to justify expenditure of health care dollars. Careful assessments of the clinical utility of markers by using comparative effectiveness research methods are urgently needed to more rigorously summarize and evaluate the evidence, but multiple factors have made such assessments difficult. The literature on tumor markers is plagued by nonpublication bias, selective reporting, and incomplete reporting. Several measures to address these problems are discussed, including development of a tumor marker study registry, greater attention to assay analytic performance and specimen quality, use of more rigorous study designs and analysis plans to establish clinical utility, and adherence to higher standards for reporting tumor marker studies. More complete and transparent reporting by adhering to criteria such as BRISQ [Biospecimen Reporting for Improved Study Quality] criteria for reporting details about specimens and REMARK [Reporting Recommendations for Tumor Marker Prognostic Studies] criteria for reporting a multitude of aspects relating to study design, analysis, and results, is essential for reliable assessment of study quality, detection of potential biases, and proper interpretation of study findings. Adopting these measures will improve the quality of the body of evidence available for comparative effectiveness research and enhance the ability to establish the clinical utility of prognostic and predictive tumor markers.

Biomarkers in Breast Cancer - An Update

The therapy of choice for breast cancer patients requiring adjuvant chemo- or radiotherapy is increasingly guided by the principle of weighing the individual effectiveness of the therapy against the associated side effects. This has only been made possible by the discovery and validation of modern biomarkers. In the last decades and in the last few years some biomarkers have been integrated in clinical practice and a number have been included in modern study concepts. The importance of biomarkers lies not merely in their prognostic value indicating the future course of disease but also in their use to predict patient response to therapy. Due to the many subgroups, mathematical models and computer-assisted analysis are increasingly being used to assess the prognostic information obtained from established clinical and histopathological factors. In addition to describing some recent computer programmes this overview will focus on established molecular markers which have already been extensively validated in clinical practice and on new molecular markers identified by genome-wide studies.

ColoGuidePro: a prognostic 7-gene expression signature for stage III colorectal cancer patients

PURPOSE

Improved prognostic stratification of patients with stage II and III colorectal cancer is warranted for postoperative clinical decision making. This study was conducted to develop a clinically feasible and robust prognostic classifier for these patients independent of adjuvant treatment.

EXPERIMENTAL DESIGN

Global gene expression profiles from altogether 387 stage II and III colorectal cancer tissue samples from three independent patient series were included in the study. ColoGuidePro, a seven-gene prognostic classifier, was developed from a selected Norwegian learning series (n = 95; no adjuvant treatment) using lasso-penalized multivariate survival modeling with cross-validation.

RESULTS

The expression signature significantly stratified patients in a consecutive Norwegian test series, in which patients were treated according to current standards [HR, 2.9 (1.1-7.5); P = 0.03; n = 77] and an external validation series [HR, 3.7 (2.0-6.8); P < 0.001; n = 215] according to survival. ColoGuidePro was also an independent predictor of prognosis in multivariate models including tumor stage in both series (HR, ≥ 3.1; P ≤ 0.03). In the validation series, which consisted of patients from other populations (United States and Australia), 5-year relapse-free survival was significantly predicted for stage III patients only (P < 0.001; n = 107). Here, prognostic stratification was independent of adjuvant treatment (P = 0.001).

CONCLUSIONS

We present ColoGuidePro, a prognostic classifier developed for patients with stage II and III colorectal cancer. The test is suitable for transfer to clinical use and has best prognostic prediction potential for stage III patients.

Molecular biology in breast cancer: should molecular classifiers be assessed by conventional tools or by gene expression arrays?

Breast cancer is a complex disease, with heterogeneous presentations and clinical courses. Standard clinico-pathological parameters, relying on single gene or protein characterization determined with sometimes poorly-reproducible technologies, have shown limitations in the classification of the disease and in the prediction of individual patient outcomes and responses to therapy. Gene-expression profiling has revealed great potential to accurately classify breast cancer and define patient prognosis and prediction to anti-cancer therapy. Nevertheless, the performance of molecular classifiers remains sub-optimal, and both technical and conceptual improvements are needed. It is likely that determining the ideal strategy for tailoring treatment of breast cancer will require a more systematic, structured and multi-dimensional approach than in the past. Besides implementing cutting-edge technologies to detect genetic and epigenetic cancer alterations, the future of breast cancer research will in all probability rely on the innovative and multilevel integration of molecular profiles with clinical parameters of the disease and patient-related factors.

Prognostic Factors for Patients with Breast Cancer: Traditional and New

At the time of breast cancer diagnosis, multiple features of the tumor are routinely assessed to evaluate for prognostic and predictive factors. Prognostic factors provide information about the patient's likely clinical course and include tumor stage (composed of lymph node status, tumor size, and presence of chest wall involvement), tumor histologic type and grade, estrogen and progesterone receptor expression, and HER2 status. These traditional prognostic factors are reviewed with particular attention to problematic areas in classification. Several newer prognostic tests may be able to provide information beyond the traditional prognostic factors and are presented.

Conference Scene: Advances in breast cancer translational research

IMPAKT is an international annual breast cancer conference that takes place in Brussels, Belgium. The conference mainly focuses on advancements in the field of translational research and the transition of laboratory findings from the bench to the clinic in the field of breast oncology. In 2012, the 4th IMPAKT conference witnessed a variety of exciting data, including new sequencing data of breast cancers, as well as the latest research in the PI3K pathway, which were presented in addition to clinically focused guideline sessions on the use of multigene signatures and molecular classification of breast cancer. There were nearly 700 attendees at the conference this year, which was regarded as a big success.

Disease related indicators for a proper choice of adjuvant treatments

Adjuvant systemic therapy (AST) clearly reduces mortality from breast cancer. The decision to recommend AST is based on prognosis and prediction. Clinically useful prognostic factors have historically been principally anatomic, based on size of the tumor and the presence or absence axillary lymph node metastases. More recently, multi-parameter assays have become incorporated into prognostic calculations, principally in patients who are ER positive. None has been established to have clinical utility in patients who are ER negative.

Adjuvant chemotherapy in luminal breast cancers

Luminal breast cancers are traditionally considered to comprise of tumors expressing estrogen receptor (ER) and represent the majority of breast cancers. These tumors are characterized by significant heterogeneity in phenotype, molecular signature, relapse patterns and therapeutic response to endocrine and chemotherapy. Whilst adjuvant endocrine therapy is standard of care in patients with tumors that express either ER and/or progesterone receptor (PR), the indication for adjuvant chemotherapy is less clear-cut. On average, ER-positive breast tumors derive less benefit from chemotherapy compared to ER-negative tumors, however there is still clearly a subset of patients with ER-positive tumors that are chemosensitive. The basis for the addition of chemotherapy to adjuvant endocrine therapy is usually guided by the clinician's estimation of prognosis and assessment of the endocrine sensitivity of the tumor. The use of chemotherapy in this setting, however, is highly variable. There is tremendous value in identifying subgroups of patients who can expect favorable outcomes with endocrine therapy and who may not require any additional therapy. Similarly, it is equally important, if not more important, to characterize patients with ER-positive disease who will derive a substantial benefit from cytotoxic chemotherapy. In this article, we aim to discuss the utility of current biomarkers used to guide decisions regarding chemotherapy in ER-positive, HER2-negative breast cancers.

Gene-expression profiling of microdissected breast cancer microvasculature identifies distinct tumor vascular subtypes

INTRODUCTION

Angiogenesis represents a potential therapeutic target in breast cancer. However, responses to targeted antiangiogenic therapies have been reported to vary among patients. This suggests that the tumor vasculature may be heterogeneous and that an appropriate choice of treatment would require an understanding of these differences.

METHODS

To investigate whether and how the breast tumor vasculature varies between individuals, we isolated tumor-associated and matched normal vasculature from 17 breast carcinomas by laser-capture microdissection, and generated gene-expression profiles. Because microvessel density has previously been associated with disease course, tumors with low (n = 9) or high (n = 8) microvessel density were selected for analysis to maximize heterogeneity for this feature.

RESULTS

We identified differences between tumor and normal vasculature, and we describe two subtypes present within tumor vasculature. These subtypes exhibit distinct gene-expression signatures that reflect features including hallmarks of vessel maturity. Potential therapeutic targets (MET, ITGAV, and PDGFRβ) are differentially expressed between subtypes. Taking these subtypes into account has allowed us to derive a vascular signature associated with disease outcome.

CONCLUSIONS

Our results further support a role for tumor microvasculature in determining disease progression. Overall, this study provides a deeper molecular understanding of the heterogeneity existing within the breast tumor vasculature and opens new avenues toward the improved design and targeting of antiangiogenic therapies.

Psychosocial and Quality of Life in Women Receiving the 21-Gene Recurrence Score Assay: The Impact of Decision Style in Women with Intermediate RS

Multigene assays such as the 21-gene recurrence score (RS) quantify risk for recurrence and potential benefit from chemotherapy in early-stage, ER+ breast cancers. Few studies have assessed the impact of testing on patient-reported outcomes such as cancer-related distress or quality of life. The few studies that have assessed these outcomes do not consider potential modifiers, such as the patients' level of involvement in the treatment decision-making process. In the current study, 81 breast cancer patients who received the RS assay completed cross-sectional surveys. We used linear multiple regression to assess whether test result, decision-making role (passive versus shared/active), and their interaction contributed to current levels of distress, quality of life, and decisional conflict. There were no associations between these variables and test result or decision-making role. However, women who received an intermediate RS and took a passive role in their care reported higher-cancer-related distress and cancer worry and lower quality of life than those who took a shared or active role. These data should be confirmed in prospective samples, as these poorer outcomes could be amenable to intervention.

Collagen gel droplet-embedded culture-drug sensitivity test and Ki67 expression in estrogen receptor-positive and HER2-negative breast cancer

Anthracyclines and taxanes are standard anticancer drugs used in breast cancer chemotherapy. In general, the efficacy of chemotherapy is lower in patients with estrogen receptor (ER)-positive tumors compared to patients with ER-negative tumors. Although less chemosensitive, ER-positive disease includes a subset of patients who significantly benefit from adjuvant chemotherapy. The collagen gel droplet-embedded culture-drug sensitivity test (CD-DST) is an in vitro chemosensitivity test that has several advantages over conventional tests. The aim of the present study was to examine the correlation between CD-DST and the expression of Ki67, an indicator of tumor proliferation, to evaluate the efficacy of anthracyclines and taxanes in patients with ER-positive and human epidermal growth factor receptor 2 (HER2)-negative breast cancer. CD-DST was performed in 68 patients with ER-positive and HER2-negative breast cancer between August 2001 and November 2006. The specimens obtained during surgery were used for the CD-DST and immunohistological examination of Ki67 expression. Chemosensitivity to the anticancer drugs adriamycin (ADM), epirubicin (EPI), docetaxel (DOC) and paclitaxel (PTX) was estimated using CD-DST. Results obtained from the CD-DST showed the chemosensitivity to each anticancer drug to be ADM, 23.7%; EPI, 75.0%; DOC, 69.2% and PTX, 43.6%. Ki67 expression was significantly higher in the group that was sensitive to DOC compared to the group that was resistant to DOC (P=0.048) and PTX (P=0.036). In addition, a significant correlation was observed between a Ki67 labeling index (LI) of >30% and chemosensitivity to PTX. In conclusion, results obtained from CD-DST and Ki67 expression levels are able to identify a subset of patients with ER-positive and HER2-negative breast cancer who exhibit sensitivity to chemotherapy, particularly to taxane therapy.

Biologic characteristics of premalignant breast disease

Breast cancer is the second leading cause of cancer death in women in the United States. While mammography and breast magnetic resonance imaging (MRI) improve detection of early disease, there remains an unmet need for biomarkers for risk stratification, early detection, prediction, and disease prognosis. A number of early breast lesions, from atypical hyperplasias to carcinomas in situ, are associated with an increased risk of developing subsequent invasive breast carcinoma. The recent development of genomic, epigenomic, and proteomic tools for tissue biomarker detection, including array CGH, RNA expression microarrays, and proteomic arrays have identified a number of potential biomarkers that both identify patients at increased risk, as well as provided insights into the pathology of early breast cancer development. This chapter focuses on the detection and application of tissue and serum biomarkers for the identification and risk stratification of early breast cancer lesions.

Study designs and statistical analyses for biomarker research

Biomarkers are becoming increasingly important for streamlining drug discovery and development. In addition, biomarkers are widely expected to be used as a tool for disease diagnosis, personalized medication, and surrogate endpoints in clinical research. In this paper, we highlight several important aspects related to study design and statistical analysis for clinical research incorporating biomarkers. We describe the typical and current study designs for exploring, detecting, and utilizing biomarkers. Furthermore, we introduce statistical issues such as confounding and multiplicity for statistical tests in biomarker research.

Why does Oncotype DX recurrence score reduce adjuvant chemotherapy use?

The Oncotype DX recurrence score (RS) reduces breast cancer adjuvant treatment utilization, but the reasons for this effect are not straightforward. We performed a retrospective chart review of 89 consecutive node-negative breast cancer patients for whom RS was ordered to facilitate adjuvant treatment decisions. By subtracting the relapse rate predicted by RS from that calculated using the Adjuvant! Online (AOL) web-based instrument, a "prognostic delta" (P∆) was determined, reflecting the difference between prognoses predicted by these two indices. Clinician interviews were conducted to evaluate the actual effect of RS on treatment decisions and its relation to P∆. Adjuvant chemotherapy use decreased from 61 to 26 % as a consequence of RS results (p < 0.0001). In multivariate analysis, RS was the only factor significantly associated with the final adjuvant treatment choice. Surprisingly, RS caused chemotherapy to be withheld even when P∆ was negative (i.e., cases in which RS predicted a less favorable outcome than AOL). The prognostic and chemotherapy predictive utilities of the RS do not fully account for its effect in reducing adjuvant chemotherapy use. Further studies are required to more fully elucidate other factors that may be responsible for this effect, including the possibility of unintended influence.

Personalized management of patients with solid cancers: moving from patient characteristics to tumor biology

PURPOSE OF REVIEW

In recent years, there has been a paradigm shift in the diagnosis as well as the treatment of solid tumors. This review will detail some of the recent findings that led to better subtyping and treatment of cancers, some of which were historically refractory.

RECENT FINDINGS

Advances in molecular biology have recently led to the rapid development of personalized cancer management. Molecular markers and gene signatures allow better risk definition and treatment prediction, thus, avoiding 'wasted toxicity'. Recent understandings in disease pathways are giving new hope to treatment of hard-to-treat cancers such as melanoma, subtypes of nonsmall cell lung cancer and several orphan tumors.

SUMMARY

This progress transposed from lab to bedside has made personalized cancer care a reality. In addition, this concept is being integrated into clinical trial designs with the enrolment of molecularly selected patients, hopefully leading to high rates of success.

TOP2A RNA expression and recurrence in estrogen receptor-positive breast cancer

The purpose of this study is to evaluate the relationship between TOP2A RNA expression and recurrence in patients with operable estrogen receptor (ER)-positive breast cancer. We evaluated TOP2A expression in a pooled analysis of four independent datasets with gene expression data including 752 patients with early stage, ER-positive, HER2-negative breast cancer, most of whom received either no adjuvant therapy or only endocrine therapy without chemotherapy. We also used an algorithm to simulate the Oncotype DX Recurrence Score (simRS) and the proliferation component of the Recurrence Score (simPS). Results are expressed as the hazard ratio (HR) for estimates of the effect of a 1SD increase in the value of the log gene expression (x + 1SD vs. x) as a continuous function. TOP2A expression was significantly associated with recurrence (HR 1.56, p < 0.0001), and after adjustment for simRS (HR 1.26, p = 0.003). TOP2A correlated somewhat with simRS (0.45), but more strongly with simPS (0.69). For those with an intermediate simRS, high TOP2A expression (above the median) was associated with significantly higher relapse rates at 5 years (HR 1.82, p = 0.007). TOP2A expression provides prognostic information in patients with ER-positive, HER2-negative breast cancer, a population known to have low incidence of TOP2A gene alterations. These findings confirm prior reports indicating that TOP2A expression provides prognostic information in ER-positive breast cancer. TOP2A expression may also be useful for identifying those with an intermediate RS who are more likely to relapse, although additional validation in datasets including measured rather than simulated RS will be required.

Predicting the response to preoperative radiation or chemoradiation by a microarray analysis of the gene expression profiles in rectal cancer

Preoperative radiotherapy or chemoradiotherapy (CRT) has become a standard treatment for patients with locally advanced rectal cancer. However, there is a wide spectrum of responses to preoperative CRT, ranging from none to complete. There has been intense interest in the identification of molecular biomarkers to predict the response to preoperative CRT, in order to spare potentially non-responsive patients from unnecessary treatment. However, no specific molecular biomarkers have yet been definitively proven to be predictive of the response to CRT. Instead of focusing on specific factors, microarray-based gene expression profiling technology enables the simultaneous analysis of large numbers of genes, and might therefore have immense potential for predicting the response to preoperative CRT. We herein review published studies using a microarray-based analysis to identify gene expression profiles associated with the response of rectal cancer to radiation or CRT. Although some studies have reported gene expression signatures capable of high predictive accuracy, the compositions of these signatures have differed considerably, with little gene overlap. However, considering the promising data regarding gene profiling in breast cancer, the microarray analysis could still have potential to improve the management of locally advanced rectal cancer. Increasing the number of patients analyzed for more accurate prediction and the extensive validation of predictive classifiers in prospective clinical trials are necessary before such profiling can be incorporated into future clinical practice.

Interest and attitudes of patients, cancer physicians, medical students and cancer researchers towards a spectrum of genetic tests relevant to breast cancer patients

The perspectives of patients and healthcare professionals towards breast cancer genetic tests that are becoming increasingly available is unexplored in Asians. We surveyed the interest and attitudes of 200 breast cancer patients, 67 cancer physicians, 485 medical students and cancer researchers towards three genetic tests, BRCA1/2 mutation, CYP2D6 genotype and Oncotype DX testing, using hypothetical scenarios. Approximately 60% of patients expressed initial interest in each genetic test, although the majority reversed their decisions once test limitations were conveyed, with <15% maintaining interest in each test. Cancer physicians were most likely to recommend BRCA1/2 mutation testing (73%) and least likely to recommend CYP2D6 genotyping (12%), while patients were more likely to choose Oncotype DX testing (28%) over CYP2D6 (21%) and BRCA1/2 testing (15%). Cost concerns, low educational level and lack of prior awareness of genetic testing were the main barriers against breast cancer genetic testing among Asian patients.