Assessing the clinical impact of prognostic factors: when is "statistically significant" clinically useful?

Clinicopathological Risk Factors of Unfavorable Outcomes in Vietnamese Women with Primary Invasive Breast Cancer: A Retrospective Cohort Study

Background

The rate of unfavorable outcomes, such as recurrence and death, in women with invasive breast cancer varies widely across countries and populations. Identifying those with high-risk profiles is critical so that early detection, prediction, and intervention can be made to improve their survival rate. Therefore, our study evaluated the rate of unfavorable outcomes and its association with clinicopathological characteristics in Vietnamese women with primary invasive breast cancer.

Methods

A retrospective open cohort study was conducted on Vietnamese women with invasive breast cancer who underwent a mastectomy and were regularly followed up by the hospitals. Kaplan-Meier method was used to estimate the rate of unfavorable outcomes to take into account the follow-up time of each patient. Univariate and multiple Cox regression analyses were conducted to examine the associations between unfavorable outcomes and clinicopathological characteristics.

Results

Among 204 women included in the data analysis, the mean age was 54.4 ± 10.9 years. The majority of patients were diagnosed with early-stage (76.5%) or locally advanced (22.5%) breast cancer. The 5-year rate of unfavorable outcomes was 12.8%, and the 8-year rate was 31.7%. Patients with advanced stages had a higher risk of unfavorable outcomes compared to those with early stages (IA, IIA, T2N1). Patients with lymph node metastases and those with triple-negative molecular classification had significantly higher rates of unfavorable outcomes.

Conclusion

Although Vietnamese women with breast cancer have a relatively low rate of unfavorable outcomes compared to other countries, findings from this study emphasize the importance of early detection and underscore the need for targeted interventions for patients with advanced stages, lymph node metastases, and triple-negative breast cancer to optimize their treatment, outcomes, and overall prognosis.

Roles of E2F family members in the diagnosis and prognosis of head and neck squamous cell carcinoma

BACKGROUND

Head and neck squamous cell carcinoma (HNSCC) is the sixth most prevalent cancer worldwide. E2Fs are a group of transcription factors involved in the carcinogenesis and progression of various cancers. However, the exact roles of each member of E2F family in the development and progression of HNSCC are still unknown.

METHODS

RNASeq and clinical follow-up information were extracted from The Cancer Genome Atlas (TCGA). The expressions of E2Fs and their roles in HNSCC progression were explored using the R software and the cBioPortal database.

RESULTS

Our results showed that the mRNA levels of E2Fs were significantly higher in HNSCC tumors than in normal tissues. E2F1, E2F3, E2F4, E2F6, and E2F7 were identified as reliable diagnostic markers. E2Fs (except for E2F3) expressions were closely related to the clinical features (excluding metastasis) of HNSCC. High E2F6 mRNA expression was an independent risk factor for the OS of female HNSCC patients. In addition, high E2F4 expression could lead to poor prognosis in HNSCC in both males and females, high expressions of E2F5, E2F6, and E2F7 were associated with poor OS of female HNSCC patients, while high E2F2 and E2F8 expressions were positively correlated with the OS of male HNSCC patients. Interestingly, E2Fs expressions had stronger associations with immune cell infiltrations in male HNSCC patients than in female HNSCC patients.

CONCLUSION

The expressions of E2Fs were found to be correlated with the progression of HNSCC. E2F1, E2F3, E2F4, E2F6, and E2F7 could be good diagnostic molecules for HNSCC. In addition, E2F6 was an independent risk factor for the prognosis of female HNSCC patients.

Personalized medicine in rheumatoid arthritis: Combining biomarkers and patient preferences to guide therapeutic decisions

The last few decades have seen major therapeutic advancements in rheumatoid arthritis (RA) therapeutics. New disease-modifying antirheumatic drugs (DMARDs) have continued to emerge, creating more choices for people. However, no therapeutic works for all patients. Each has its own inherent benefits, risks, costs, dosing, and monitoring considerations. In parallel, there has been a focus on personalized medicine initiatives that tailor therapeutic decisions to patients based on their unique characteristics or biomarkers. Personalized effect estimates require an understanding of a patient's baseline probability of response to treatment and data on the comparative effectiveness of the available treatments. However, even if accurate risk prediction models are available, trade-offs often still need to be made between treatments. In this paper, we review the history of RA therapeutics and progress that has been made toward personalized risk predictive models for DMARDs, outlining where knowledge gaps still exist. We further review why patient preferences play a key role in a holistic view of personalized medicine and how this links with shared decision-making. We argue that a "preference misdiagnosis" may be equally important as a medical misdiagnosis but is often overlooked.

The Present and Future of Clinical Management in Metastatic Breast Cancer

Regardless of the advances in our ability to detect early and treat breast cancer, it is still one of the common types of malignancy worldwide, with the majority of patients decease upon metastatic disease. Nevertheless, due to these advances, we have extensively characterized the drivers and molecular profiling of breast cancer and further dividing it into subtypes. These subgroups are based on immunohistological markers (Estrogen Receptor-ER; Progesterone Receptor-PR and Human Epidermal Growth Factor Receptor 2-HER-2) and transcriptomic signatures with distinct therapeutic approaches and regiments. These therapeutic approaches include targeted therapy (HER-2+), endocrine therapy (HR+) or chemotherapy (TNBC) with optional combination radiotherapy, depending on clinical stage. Technological and scientific advances in the identification of molecular pathways that contribute to therapy-resistance and establishment of metastatic disease, have provided the rationale for revolutionary targeted approaches against Cyclin-Dependent Kinases 4/6 (CDK4/6), PI3 Kinase (PI3K), Poly ADP Ribose Polymerase (PARP) and Programmed Death-Ligand 1 (PD-L1), among others. In this review, we focus on the comprehensive overview of epidemiology and current standard of care treatment of metastatic breast cancer, along with ongoing clinical trials. Towards this goal, we utilized available literature from PubMed and ongoing clinical trial information from clinicaltrials.gov to reflect the up to date and future treatment options for metastatic breast cancer.

Incorporation of TILs in daily breast cancer care: how much evidence can we bear?

One of the most important developments in the breast cancer field has been an improved understanding of prognostic and predictive biomarkers, of which TILs are increasingly gaining importance. The evaluation of TILs by light microscopy on a H&E-stained section is workable in a daily practice setting. Reproducibility of reporting TILs is good, but heterogeneity is a cause of variation. TILs provide clinicians with important prognostic information for patients with TNBC, as early-stage TNBC with high TILs have > 98% 5-year survival and TILs predict benefit to immunotherapy. Importantly, while TILs do not have level of evidence IA, TILs should be used as a prognostic factor with caution and with other accepted prognostic variables, such as tumour size and lymph node status, to inform clinicians and patients on their treatment options. A framework on how to use the TILs in daily practice is proposed, including a co-assessment with PD-L1 for its predictive role to immunotherapy.

Radioproteomics in Breast Cancer: Prediction of Ki-67 Expression With MRI-based Radiomic Models

RATIONALE AND OBJECTIVES

We aimed to investigate the value of magnetic resonance image (MRI)-based radiomics in predicting Ki-67 expression of breast cancer.

METHODS

In this retrospective study, 159 lesions from 154 patients were included. Radiomic features were extracted from contrast-enhanced T1-weighted MRI (C+MRI) and apparent diffusion coefficient (ADC) maps, with open-source software. Dimension reduction was done with reliability analysis, collinearity analysis, and feature selection. Two different Ki-67 expression cut-off values (14% vs 20%) were studied as reference standard for the classifications. Input for the models were radiomic features from individual MRI sequences or their combination. Classifications were performed using a generalized linear model.

RESULTS

Considering Ki-67 cut-off value of 14%, training and testing AUC values were 0.785 (standard deviation [SD], 0.193) and 0.849 for ADC; 0.696 (SD, 0.150) and 0.695 for C+MRI; 0.755 (SD, 0.171) and 0.635 for the combination of both sequences, respectively. Regarding Ki-67 cut-off value of 20%, training and testing AUC values were 0.744 (SD, 0.197) and 0.617 for ADC; 0.629 (SD, 0.251) and 0.741 for C+MRI; 0.761 (SD, 0.207) and 0.618 for the combination of both sequences, respectively.

CONCLUSION

ADC map-based selected radiomic features coupled with generalized linear modeling might be a promising non-invasive method to determine the Ki-67 expression level of breast cancer.

Prominent Prognostic Factors in Aggressive Breast Cancer: A Review

Context: Breast cancer (BC) is the most common cancer in women worldwide. Hereditary susceptibility created by mutations in autosomal dominant genes is responsible for 5 to 10% of all BC cases in women. Recent studies have identified genes associated with increased risk for aggressive BC, providing the basis for better risk management. Evidence Acquisition: The latest information in National Center for Biotechnology Information (NCBI), Google Scholar, ScienceDirect, and Scopus were the main databases for finding articles. A combination of keywords of ‘metastasis’, ‘invasion’, ‘aggressive breast cancer’, ‘prognostic factor’, ‘mutation’, and ‘cancer treatment’ was searched in the databases to identify related articles. Titles and abstracts of the articles were studied to choose the right articles. Results: Mutations in breast cancer type 1 susceptibility protein (BRCA1) and breast cancer type 2 susceptibility protein (BRCA2) genes are two central players related to the high risk of BC. Mutation in tumor protein p53 (TP53) is another important mutation that leads to triple-negative BC. Although the majority of BC types are not associated with high-throughput mutant genes such as BRCA1, BRCA2, and TP53, they are associated with low-throughput genes, including DNA repair protein Rad50 (RAD50), Nijmegen breakage syndrome gene (NBS1), checkpoint kinase 2 (CHEK2), BRCA1-interacting protein 1 (BRIP1), E-cadherin gene (CDH1) and PALB2, UCHL1, aldehydedehydrogenase1A3 (ALDH1A3), androgen receptor (AR), 5-bisphosphate 3-kinase (PIK3CA), phosphatidylinositol-4, and luminal gene expression that are generally mutated in the global population. High tumor mutational burden (TMB) was associated with improved progression-free survival. Conclusions: The lymph node status, early tumor size, ER, PR, human epidermal growth factor receptor-2 (HER2), and Ki-67 are conventional prognostic factors for BC. However, these factors cannot exactly predict the aggressive behavior of BC. Hence, in this review, we discussed new prognostic factors of aggressive BCs that are useful for the treatment of patients with BC.

Risk stratification and prognostic factors in patients with unresectable undifferentiated carcinoma of the pancreas

BACKGROUND

Undifferentiated carcinoma (UC) of the pancreas has been considered a highly aggressive malignancy. However, only a few studies have systematically described the clinical course of UC patients. The aim of this study was to clarify the prognosis and construct a prognostic model for patients with unresectable UC.

METHODS

This study was conducted at 17 institutions in Japan, and a total of 55 patients were analyzed.

RESULTS

The median overall survival (OS) of patients with unresectable UC was 3.95 months. In the multivariate Cox proportional hazards (CPH) model, age ≥65 years, Eastern Cooperative Oncology Group performance status (ECOG PS) ≥2, and C-reactive protein (CRP) >10 mg/L were independent prognostic factors for OS (age ≥65 years: hazard ratio [HR], 2.732; 95% confidence interval [CI], 1.353-5.515; ECOG PS ≥ 2: HR, 7.866; 95% CI, 1.981-31.241; CRP >10 mg/L: HR, 1.956; 95% CI, 1.013-3.775). Based on the β coefficients from the CPH model, the prognostic scores were defined as follows: age ≥65 years (3 points), ECOG PS ≥ 2 (6 points), and CRP >10 ml/L (2 points). The final prognostic model was the sum of the points. The derived prognostic model stratified patients into high-risk (score ≥4) and low-risk (score 0-3) groups, with significant differences in OS (1.45 vs. 8.19 months, respectively; p < 0.001).

CONCLUSIONS

The prognostic model stratified patients into high-risk and low-risk groups. These findings suggest that this model can serve as a tool for patient information and decision-making with regard to the therapeutic strategy for UC.

Correlation between Androgen Receptor Expression and Immunohistochemistry Type as Prognostic Factors in a Cohort of Breast Cancer Patients: Result from a Single-Center, Cross Sectional Study

Background: We investigated the correlation between the androgen receptor (AR) and immunohistochemistry (IHC) as a prognostic factor in breast cancer (BC). AR is expressed in 60–80% of BC. Methods: We evaluated the prognostic values of AR expression among 143 patients with BC for 36 months. The protocol was amended to measure androgen, estrogen and progesterone receptor expression by IHC and the percentage of hormone positive nuclei was quantified. We determined and quantified the Her2/neu status using IHC and in situ hybridization. The methodology consisted in using a Kaplan–Meier analysis and restricted mean survival time up to 36 months. The principal endpoints of the study were overall survival (OS) and progression free survival (PFS). Results: 57% of patients (n = 82) from our group had AR+ (≥ 1%). Patients with AR+ had better OS, 35.50 vs. 33.40 months, with p = 0.027. Moreover, PFS was prolonged for patients AR+, 32.60 vs. 30.50 months, with p = 0.38. Triple negative breast cancer (TNBC) patients had lower OS and no difference was observed for PFS. Conclusions: Both OS and PFS were favorably influenced by the presence of AR. TNBC had worse outcomes compared with patients with hormonal or/and Her 2/neu positive disease in terms of OS.

Defining Clinical Utility of Tumor Biomarker Tests: A Clinician's Viewpoint

Tumor biomarker tests (TBTs) are used to guide therapeutic strategies for patients with cancer. However, the regulatory environment for TBTs in the United States is inconsistent and, in general, TBTs are poorly valued. The National Academy of Medicine has recommended that TBTs should not be used in general practice until they are shown to have analytical validity and clinical utility. The latter term, first coined by the Evaluation of Genomic Applications in Practice and Prevention Initiative, has been widely stated but is indeterminately defined. In considering whether a TBT has clinical utility, several factors need to be considered: (1) What is the intended use of the TBT? (2) What are the end points that are used to determine clinical utility? (3) How substantial does the difference in end points between groups defined by the TBT need to be to determine therapeutic strategies? (4) What is the risk tolerance of the stakeholders? and (5) Who are the stakeholders that make the decision? For all these factors, the data used to consider clinical utility must be derived from level I evidence studies. In conclusion, there is no strict definition of clinical utility for a TBT. However, consideration of these factors will lead to more objective conclusions. Doing so will facilitate value-based decisions regarding whether a TBT should be used to guide patient care.

Examination of the Biomark assay as an alternative to Oncotype DX for defining chemotherapy benefit

Currently the 21-gene recurrence score (RS) assay called Oncotype DX is recommended by the National Comprehensive Cancer Network guideline for defining the benefit of chemotherapy. To overcome the cost disadvantages of the Oncotype DX assay and the turnaround time, a multigene assay was examined to compare the correlation of the RS and the predicted score (PS) of the present study. Paraffin-embedded tissues of 50 cases with early-stage estrogen receptor (ER)-positive breast cancer, who underwent the Oncotype DX test were used. A total of 149 candidate genes with high correlation to the RS were identified, in another project (Lee et al, unpublished data). Reverse transcription-quantitative polymerase chain reaction biomark assays were conducted using the dynamic array integrated fluidic circuit and the correlation analysis was performed with BRB ArrayTools. A predictive model was developed by the coefficient and gene expression, and 41 genes were identified. If the cut-off was ≥18, the predicted model was 18/50 cases, and the RS was 19, indicating that the differential rate of predicted response against RS was 2%. If the cutoff was ≥11, the predicted model was 38/50 cases and the RS was 34, indicating a difference of 8%. Genes common to the Oncotype DX and the Biomark assay include marker of proliferation Ki-67, aurora kinase A, Erb-B2 receptor tyrosine kinase 2, glutathione S-transferase Mu 1, estrogen receptor 1, progesterone receptor, B-cell lymphoma 2, signal peptide CUB domain EGF-like 2 and 5 reference genes. The remaining 28 genes are involved in various pathways and functions. This result indicates that there is a significant correlation between PS and RS scores, although validation of results is required to accurately determine the risk of distant recurrence. The Biomark assay is an easy and inexpensive way to measure mRNA expression. The present study demonstrates the possibility of the Biomark assay as an alternative for defining chemotherapy benefit in individual patients with ER-positive early-stage breast cancer.

Evaluation of human serum S100B protein as a prognostic factor in nonmetastatic breast cancer patients

Aim: The aim of this study was the evaluation of S100B prognostic role in nonmetastatic breast cancer patients, excluding confounding factors. Patients & method: 79 breast cancer patients, who fulfilled inclusion and exclusion criteria, were enrolled in the study. Before any adjuvant chemotherapy or surgery, the serum level of S100B was determined. All patients were followed up for 5 years, particularly regarding cancer recurrence and patients’ survival. Results: 42% of the patients experienced no recurrence after 5 years and cumulative risk of recurrence was 0.86. There was no significant correlation between serum level of protein S100B and recurrence rate in 5 years (p = 0.59). Conclusion: According to the results, the serum levels of S100B protein may have no prognostic role in nonmetastatic breast cancer.

An alternative method to analyse the biomarker-strategy design

Recent developments in genomics and proteomics enable the discovery of biomarkers that allow identification of subgroups of patients responding well to a treatment. One currently used clinical trial design incorporating a predictive biomarker is the so‐called biomarker strategy design (or marker‐based strategy design). Conventionally, the results from this design are analysed by comparing the mean of the biomarker‐led arm with the mean of the randomised arm. Several problems regarding the analysis of the data obtained from this design have been identified in the literature. In this paper, we show how these problems can be resolved if the sample sizes in the subgroups fulfil the specified orthogonality condition. We also propose a different analysis strategy that allows definition of test statistics for the biomarker‐by‐treatment interaction effect as well as for the classical treatment effect and the biomarker effect. We derive equations for the sample size calculation for the case of perfect and imperfect biomarker assays. We also show that the often used 1:1 randomisation does not necessarily lead to the smallest sample size. In addition, we provide point estimators and confidence intervals for the treatment effects in the subgroups. Application of our method is illustrated using a real data example.

Relative efficiency of precision medicine designs for clinical trials with predictive biomarkers

Prospective randomized clinical trials addressing biomarkers are time consuming and costly, but are necessary for regulatory agencies to approve new therapies with predictive biomarkers. For this reason, recently, there have been many discussions and proposals of various trial designs and comparisons of their efficiency in the literature. We compare statistical efficiencies between the marker-stratified design and the marker-based precision medicine design regarding testing/estimating 4 hypotheses/parameters of clinical interest, namely, treatment effects in each marker-positive and marker-negative cohorts, marker-by-treatment interaction, and the marker's clinical utility. As may be expected, the stratified design is more efficient than the precision medicine design. However, it is perhaps surprising to find out how low the relative efficiency can be for the precision medicine design. We quantify the relative efficiency as a function of design factors including the marker-positive prevalence rate, marker assay and classification sensitivity and specificity, and the treatment randomization ratio. It is interesting to examine the trends of the relative efficiency with these design parameters in testing different hypotheses. We advocate to use the stratified design over the precision medicine design in clinical trials with predictive biomarkers.

Safety and efficacy evaluation of pertuzumab in patients with solid tumors

BACKGROUND

The development of targeted therapies benefits patients with certain markers in the treatment of breast cancer. Pertuzumab is a novel humanized monoclonal antibody that blocks human epidermal growth factor receptor 2 (HER2) dimerization. The Food and Drug Administration has approved pertuzumab in combination with trastuzumab and docetaxel for the treatment of patients with HER2-positive metastatic breast cancer.

METHODS

To assess the safety and efficacy profile of pertuzumab, we searched PubMed and Embase (articles from January 1966 to January 2015) using the keyword "pertuzumab".

RESULTS

Fourteen eligible studies were included in our final analysis. From the results of our analysis, diarrhea (56.9%, 95% confidence interval [CI] 49.6%-63.9%), nausea (34.0%, 95% CI 27.7%-40.8%), and rash (25.6%, 95% CI 20.8%-31.0%) were the most common adverse effects in pertuzumab alone and pertuzumab-based therapies. Based on randomized controlled clinical trials, diarrhea (odds ratio [OR] 2.310, 95% CI 1.818-2.936), rash (OR 1.848, 95% CI 1.094-3.122), and febrile neutropenia (OR 1.672, 95% CI 1.130-2.474) were of statistical significance, which meant that pertuzumab played a prominent role in the incidence of diarrhea. Meanwhile, pertuzumab showed its effective role in cancer control and lifetime prolongation.

CONCLUSION

In conclusion, considering that the common adverse effects for pertuzumab are gastrointestinal and skin toxicities, which are easier to handle than other toxicities, pertuzumab is a safe and effective drug for patients with solid tumors.

Retrospective evaluation of premenopausal hormone-sensitive breast cancer patients treated with adjuvant gonadotropin-releasing hormone analogue: Anatolian Society of Medical Oncology (ASMO) study

AIM

The goal of this study is to evaluate possible factors affecting the survival of patients treated with gonadotropin-releasing hormone (GnRH) analogues.

METHODS

Demographic characteristics, treatment modalities, overall survival (OS) and the possible factors affecting the survival a total of 554 premenopausal breast cancer patients in Turkey evaluated retrospectively.

RESULTS

The median duration of GnRH analogues use was 22 ± 13.6 (range, 1-87) months. Patients were divided into three groups according to the duration of GNRH analogues use; 4-12 months (Group A), 13-24 months (Group B) and ≥25 months (Group C). Overall, 530 patients were analyzed; 23.2%, 45.8%, 30.9% of the patients were in Group A, B and C, respectively. The median follow-up duration was 34 ± 30.3 (range, 4-188) months. The OS in patients ≤35 years of age was found to be significantly longer than that of patients >35 years of age in Group B (log rank, P = 0.023). The disease-free survival of the patients in Group A was significantly shorter than that of patients in Group C (log rank, P = 0.003). The OS of Group A patients was significantly shorter in comparison to that of Group B and Group C patients (log rank, P = 0.000) and the OS of Group B patients was significantly shorter than Group C (log rank, P = 0,000).

CONCLUSION

There is currently no definite data on the optimal duration of GnRH analogues use. One of the important results of this study that will provide an insight to the future studies is the improvement gained in OS by the increase in the duration of GnRH analogues use.

Identification and evaluation of network modules for the prognosis of basal-like breast cancer

Purpose

Basal-like breast cancer (BLBC) is a molecular subtype of breast cancer associated with poor clinical outcome, although some patients with BLBC experience long-term survival. Apart from nodal status, current clinical/histopathological variables show little capacity to identify BLBC patients at either high- or low-risk of disease recurrence. Accordingly, we sought to develop a network based genomic predictor for predicting the outcome of patients with BLBC.

Experimental Design

We performed network analysis on global gene expression profiling data of BLBCs, and identified BLBC network modules associated with AP-1 transcription, G-protein coupled receptors, and T-, B-, and NK-cells that are significant predictors of BLBC patient survival.

Results

In gene expression and tissue microarray (TMA) validation cohorts of 210 and 102 BLBC patients, respectively, the identified network modules were robustly associated with patient outcome. In the gene expression validation cohort, the Kaplan-Meier estimate for 10-year survival in the low-risk group was 90%, whereas in the high-risk group it was a 56%. In the TMA cohort, the Kaplan-Meier estimate for 10-year survival in the low-risk group was 98%, whereas in the high-risk group it was 71%.

Conclusions

The capacity to distinguish between patients with BLBC at high- or low-risk of recurrence at the time of diagnosis could permit timely intervention with more aggressive therapeutic regimens in those patients predicted to be high-risk, and to avoid such therapy in low-risk patients.

Human 3β-hydroxysteroid dehydrogenase type 1 in human breast cancer: clinical significance and prognostic associations

Active sex steroids including estrogens and androgens are locally produced from circulating inactive steroids by various steroid-metabolizing enzymes, and play pivotal roles in the progression of hormone-dependent breast cancers. Human 3β-hydroxysteroid dehydrogenase type 1 (3β-HSD type 1) is a critical enzyme in the formation of all classes of active steroid hormones, and is also involved in the inactivation of potent androgen dihydrotestosterone (DHT). Therefore, this enzyme is suggested to modulate active sex steroid production or inactivation, with a role in hormone-dependent breast cancer. The purpose of this study was to investigate the clinical significance of 3β-HSD type 1 in human breast cancer. Using immunohistochemistry (IHC), we evaluated 3β-HSD type 1 expression in 161 human breast cancers and analyzed correlations of 3β-HSD type 1 expression with various clinicopathological factors. Of 161 breast cancer cases, 3β-HSD type 1 expression in cancer cells was detected in 119 cases (73.9%), and was positively correlated with estrogen receptor (ER)-positivity but not HER-2 status. In ER-positive cases (n = 130), 3β-HSD type 1 expression was inversely correlated with invasive tumor size (P = 0.0009), presence of invasive region (P = 0.0107), and lymphatic involvement (P = 0.0004). 3β-HSD type 1 expression was significantly associated with decreased risk of recurrence or improved prognosis by both univariate (P = 0.0003 and P = 0.009, respectively) and multivariate (P = 0.027 and P = 0.023, respectively) analyses. Our findings indicate that this enzyme is a prognostic factor in hormone-dependent breast cancer.

Biomarkers for efficacy of adjuvant chemotherapy following complete resection in NSCLC stages I-IIIA

Biomarkers may be useful when deciding which nonsmall cell lung cancer (NSCLC) patients may benefit from adjuvant chemotherapy following complete resection and which chemotherapeutic agents may be used preferably in individual patients in order to maximise survival.

A literature search covering the period from 2003 to May, 2014 was conducted using PubMed and the following search terms: “non-small cell lung cancer”, “NSCLC”, “adjuvant chemotherapy”, “randomized”, “randomised”, “biomarkers”, “prognostic”, “predictive”. This review focuses on current knowledge of biomarkers for prognosis or efficacy of adjuvant treatment following complete resection in stage I–IIIA NSCLC patients.

This review includes results on 18 different biomarkers and five gene profiles. A statistically significant prognostic impact was reported for: iNTR, TUBB3, RRM1, ERCC1, BRCA1, p53, MRP2, MSH2, TS, mucin, BAG-1, pERK1/2, pAkt-1, microRNA, TopIIA, 15-gene profile, 92-gene profile, 31-gene profile and 14-gene profile. A statistically significant predictive impact was reported for: ERCC1, p53, MSH2, p27, TUBB3, PARP1, ATM, 37-gene profile, 31-gene profile, 15-gene profile and 92-gene profile.

Uncertainties regarding the optimal analysis method and cut-off levels for the individual markers may blur the prognostic or predictive signals. None of the possible predictive markers have been validated in prospective trials. Thus, there are no biomarkers ready to use in an adjuvant setting in NSCLC.

Further investigation and validation is required to explore biomarkers in completely resected NSCLC stage I–IIIAhttp://ow.ly/M0leE

E2F4 Program Is Predictive of Progression and Intravesical Immunotherapy Efficacy in Bladder Cancer

Bladder cancer is a common malignant disease, with non-muscle-invasive bladder cancer (NMIBC) representing the majority of tumors. This cancer subtype is typically treated by transurethral resection. In spite of treatment, up to 70% of patients show local recurrences. Intravesical BCG (Bacillus Calmette-Guerin) immunotherapy has been widely used to treat NMIBC, but it fails to suppress recurrence of bladder tumors in up to 40% of patients. Therefore, the development of prognostic markers is needed to predict the progression of bladder cancer and the efficacy of intravesical BCG treatment. This study demonstrates the effectiveness of an E2F4 signature for prognostic prediction of bladder cancer. E2F4 scores for each sample in a bladder cancer expression dataset were calculated by summarizing the relative expression levels of E2F4 target genes identified by ChIP-seq, and then the scores were used to stratify patients into good- and poor-outcome groups. The molecular signature was investigated in a single bladder cancer dataset and then its effectiveness was confirmed in two meta-bladder datasets consisting of specimens from multiple independent studies. These results were consistent in different datasets and demonstrate that the E2F4 score is predictive of clinical outcomes in bladder cancer, with patients whose tumors exhibit an E2F4 score >0 having significantly shorter survival times than those with an E2F4 score <0, in both non-muscle-invasive, and muscle-invasive bladder cancer. Furthermore, although intravesical BCG immunotherapy can significantly improve the clinical outcome of NMIBC patients with positive E2F4 scores (E2F4>0 group), it does not show significant treatment effect for those with negative scores (E2F4<0 group).

IMPLICATIONS

The E2F4 signature can be applied to predict the progression/recurrence and the responsiveness of patients to intravesical BCG immunotherapy in bladder cancer.

Predictive and prognostic cancer biomarkers revisited

While both prognostic and predictive cancer biomarkers predict clinical outcome, the term 'predictive biomarker' is reserved for the association of a specific therapy with a specific clinical outcome. The advent of genomic signatures and next generation sequencing as candidate predictive biomarkers has led to lengthy and expensive processes for biomarker qualification. The urgency to bring novel predictive cancer biomarkers to practice faster and cheaper requires strategies to lower the bar to biomarker implementation. Three strategies are suggested: identify biomarkers closely coupled to biologic mechanism associated with the clinical endpoint and scalable from cells to humans; identify biomarkers that can be reliably detected and quantified; and assess biomarkers by capacity to reduce toxicity as well as to increase therapy efficacy. Biomarker selection directly and closely related to production of end points by biologic mechanism demonstrated by a ladder of evidence should require less burden of proof clinically than biomarkers that are merely associative.

A program to identify prognostic and predictive gene signatures

BACKGROUND

The advent of high-throughput technologies to profile human tumors has generated unprecedented insight into our molecular understanding of cancer. However, analysis of such high dimensional data is challenging and requires significant expertise which is not routinely available to many cancer researchers.

RESULTS

To overcome this limitation, we developed a freely accessible and user friendly Program to Identify Molecular Signatures (PIMS). Importantly, such signatures allow important insight into cancer biology, as well as provide clinical tools to identify potential biomarkers that might provide means to accurately stratify patients into different risk or treatment groups. We evaluated the performance of PIMS by identifying and testing predictive and prognostic gene signatures for breast cancer, using multiple breast tumor microarray cohorts representing hundreds of patients. Importantly, PIMS identified signatures classified patients into high and low risk groups with at least similar performance to other commonly used gene signature selection techniques.

CONCLUSIONS

Our program is contained entirely within a Microsoft Excel file and therefore requires no installation of any additional programs or training. Hence, PIMS provides an accessible tool for cancer researchers to identify predictive and prognostic gene signatures to advance their research.

Multifaceted role of EZH2 in breast and prostate tumorigenesis: epigenetics and beyond

Overexpression of EZH2 and other PRC2 subunits, such as SUZ12, is associated with tumor progression and poor prognosis in several human malignancies. Nevertheless, the underlying mechanisms driving aberrant EZH2 expression are poorly understood. This review provides molecular insights into the essential role of EZH2 in breast and prostate tumorigenesis. We addressed the current understanding on the oncogenic role of EZH2, with an emphasis on: (1) the less known PRC2-independent role of EZH2 in gene activation, in addition to its canonical role in transcriptional silencing as a histone methyltransferase catalyzing the trimethylation of histone H3 at lysine 27; (2) causes and consequences of its deregulation in tumor cells and; (3) collaboration of EZH2 with other epigenetic and hormone receptor-mediated oncogenic signaling pathways. We also summarize how EZH2 has emerged as a promising therapeutic target in hormone-refractory cancers and the prospects for integrating EZH2 blockade with available pharmacological inhibitors.

Predictive tests for individualization of pharmacological cancer treatment

BACKGROUND

The selection of cancer drugs for an individual patient is still based mostly on cancer type and stage. Predictive tests are needed to make individualized and more efficient pharmacological cancer treatment possible.

OBJECTIVE

To provide an overview of available, possible future development and principles for development of predictive tests for individualized selection of cancer drugs.

METHODS

Overview of published data.

RESULTS/CONCLUSION

Despite increased knowledge in cancer biology, only limited progress has been made in the development and use of predictive tests. However, rapid progress in this field will be possible using already available and emerging technologies, but requires a paradigm shift in principles for the development and use of cancer drugs. Assessment of drug activity in intact tumor cells and tumor cell gene expression signatures are considered to have greatest potential for the development of versatile predictive tests.

Challenges in the development and reimbursement of personalized medicine-payer and manufacturer perspectives and implications for health economics and outcomes research: a report of the ISPOR personalized medicine special interest group

BACKGROUND

Personalized medicine technologies can improve individual health by delivering the right dose of the right drug to the right patient at the right time but create challenges in deciding which technologies offer sufficient value to justify widespread diffusion. Personalized medicine technologies, however, do not neatly fit into existing health technology assessment and reimbursement processes.

OBJECTIVES

In this article, the Personalized Medicine Special Interest Group of the International Society for Pharmacoeconomics and Outcomes Research evaluated key development and reimbursement considerations from the payer and manufacturer perspectives.

METHODS

Five key areas in which health economics and outcomes research best practices could be developed to improve value assessment, reimbursement, and patient access decisions for personalized medicine have been identified.

RESULTS

These areas are as follows: 1 research prioritization and early value assessment, 2 best practices for clinical evidence development, 3 best practices for health economic assessment, 4 addressing health technology assessment challenges, and 5 new incentive and reimbursement approaches for personalized medicine.

CONCLUSIONS

Key gaps in health economics and outcomes research best practices, decision standards, and value assessment processes are also discussed, along with next steps for evolving health economics and outcomes research practices in personalized medicine.

Expression of cyclin D1a and D1b as predictive factors for treatment response in colorectal cancer

Background:

The aim of this study was to investigate the value of the cyclin D1 isoforms D1a and D1b as prognostic factors and their relevance as predictors of response to adjuvant chemotherapy with 5-fluorouracil and levamisole (5-FU/LEV) in colorectal cancer (CRC).

Methods:

Protein expression of nuclear cyclin D1a and D1b was assessed by immunohistochemistry in 335 CRC patients treated with surgery alone or with adjuvant therapy using 5-FU/LEV. The prognostic and predictive value of these two molecular markers and clinicopathological factors were evaluated statistically in univariate and multivariate survival analyses.

Results:

Neither cyclin D1a nor D1b showed any prognostic value in CRC or colon cancer patients. However, high cyclin D1a predicted benefit from adjuvant therapy measured in 5-year relapse-free survival (RFS) and CRC-specific survival (CSS) compared to surgery alone in colon cancer (P=0.012 and P=0.038, respectively) and especially in colon cancer stage III patients (P=0.005 and P=0.019, respectively) in univariate analyses. An interaction between treatment group and cyclin D1a could be shown for RFS (P=0.004) and CSS (P=0.025) in multivariate analysis.

Conclusion:

Our study identifies high cyclin D1a protein expression as a positive predictive factor for the benefit of adjuvant 5-FU/LEV treatment in colon cancer, particularly in stage III colon cancer.

Improving colorectal cancer management: the potential of proteomics

Colorectal cancer (CRC) is the third most common cancer worldwide. Successful treatment is heavily dependent on tumor stage at the time of detection, but unfortunately CRC is often only detected in advanced stages. New biomarkers in the form of genes or proteins that can be used for diagnosis, prognostication, follow-up, and treatment selection and monitoring could be of great benefit for the management of CRC. Furthermore, proteins could prove valuable new targets for therapy. Therefore, clinical proteomics has gained a lot of scientific interest in this regard. To get an overall insight into the extent to which this research has contributed to a better management of CRC, we give a comprehensive overview of the results of proteomics research on CRC, focusing on expression proteomics, in other words, protein profiling studies. Furthermore, we evaluate the potential of the discriminating proteins identified in this research for clinical use as biomarkers for (early) diagnosis, prognosis and follow-up of CRC or as targets for new therapeutic regimens.

Cost-effectiveness of a 21-gene recurrence score assay versus Canadian clinical practice in women with early-stage estrogen- or progesterone-receptor-positive, axillary lymph-node negative breast cancer

BACKGROUND

A 21-gene recurrence score (RS) assay may inform adjuvant systematic treatment decisions in women with early stage breast cancer. We sought to investigate the cost effectiveness of using the RS-assay versus current clinical practice (CCP) in women with early-stage estrogen- or progesterone-receptor-positive, axilliary lymph-node negative breast cancer (ER+/ PR + LN- ESBC) from the perspective of the Canadian public healthcare system.

METHODS

We developed a Markov model to project the lifetime clinical and economic consequences of ESBC. We evaluated adjuvant therapy separately in post- and pre-menopausal women with ER+/ PR + LN- ESBC. We assumed that the RS-assay would reclassify pre- and post-menopausal women among risk levels (low, intermediate and high) and guide adjuvant systematic treatment decisions. The model was parameterized using 7 year follow up data from the Manitoba Cancer Registry, cost data from Manitoba administrative databases, and secondary sources. Costs are presented in 2010 CAD. Future costs and benefits were discounted at 5%.

RESULTS

The RS-assay compared to CCP generated cost-savings in pre-menopausal women and had an ICER of $60,000 per QALY gained in post-menopausal women. The cost effectiveness was most sensitive to the proportion of women classified as intermediate risk by the RS-assay who receive adjuvant chemotherapy and the risk of relapse in the RS-assay model.

CONCLUSIONS

The RS-assay is likely to be cost effective in the Canadian healthcare system and should be considered for adoption in women with ER+/ PR + LN- ESBC. However, ongoing assessment and validation of the assay in real-world clinical practice is warranted.

Overexpression of class III beta tubulin and amplified HER2 gene predict good response to paclitaxel and trastuzumab therapy

Through this study, we aimed to validate several biomarkers that have been known to possibly predict the outcomes of the trastuzumab and paclitaxel (TP). Human epidermal growth factor 2 (HER2) positive metastatic breast cancer (MBC) patients who had been treated with TP in single institute from 2006 to 2009 were included in this study. For procured formalin fixed paraffin embedded tumor tissues, HER2 amplification index (AI) and polymorphisms of the immunoglobulin G fragment C receptors (FCGR) were assessed as biomarkers to the trastuzumab and expression of class III beta tubulin (bTubIII) was evaluated as a predictive factor to the paclitaxel. Of 46 patients treated with TP, 27 patients could be evaluated for HER2 AI, 31 for bTubIII, and 26 for FCGR gene polymorphism. The median of the HER2 AI was 5.0 (range, 1.4−15.5) and a higher HER2 AI (≥5.0) was significantly correlated with better response rate (RR) (80% vs. 42%, P = 0.049) and longer progression-free survival (PFS) (13.6 vs. 6.9 months, P = 0.023). High bTubIII expression showed higher RRs than did low expression (81% vs. 40%, P = 0.040) in addition to longer PFS (16.2 months vs. 8.8 months, P = 0.04). However, polymorphisms in FCGR 2A-H131R or FCGR 3A-V158F were not predictive of RR or PFS. Our results suggest that a high HER2 AI and high bTubIII expression could be predictive of the outcomes to TP therapy but no evidence was found in terms of FCGR polymorphisms.

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.

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.

A gene signature for predicting outcome in patients with basal-like breast cancer

Basal-like breast cancer is a molecular subtype of breast cancer with a poor prognosis. Follow-up studies of long-term outcome in these patients, demonstrates they can be separated into two clinical groups: those who succumb to their disease within the first 5 years and those expected to show excellent long term survival. Currently available clinical/histopathological variables as well as molecular signatures show little capacity to identify basal breast cancer patients with either a high or low risk of disease relapse. Using data derived from 85 basal-like breast cancer patients, we identified a 14-gene signature, which we subsequently validated on an additional 49 basal breast cancer patient set. The ability to distinguish between these two sub-groups of basal breast cancer patients at the time of initial diagnosis would permit tailoring aggressive therapeutic regimens to those patients with a poor prognosis and conversely avoid such therapy in low risk patients.

Gene expression of the mismatch repair gene MSH2 in primary colorectal cancer

Microsatellite instability (MSI) is caused by defective mismatch repair (MMR) and is one of the very few molecular markers with proven clinical importance in colorectal cancer with respect to heredity, prognosis, and treatment effect. The gene expression of the MMR gene MSH2 may be a quantitative marker for the level of MMR and a potential molecular marker with clinical relevance. The aim was to investigate the gene expression of MSH2 in primary operable colorectal cancer in correlation with MSI, protein expression, and promoter hypermethylation. In a cohort of 210 patients, the primary tumor and lymphnode metastases were analyzed with immunohistochemistry, methylation and MSI analyses, and quantitative polymerase chain reaction (PCR). The median gene expression of MSH2 was 1.00 (range 0.16-11.2, quartiles 0.70-1.51) and there was good agreement between the gene expression in primary tumor and lymph node metastasis (Spearman's rho = 0.57, p < 0.001, n = 73). The validity of gene expression analysis was made probable by a significant correlation to protein expression (p = 0.005). MSI was most often caused by deficient MLH1 and was not correlated to MSH2 expression. Hypermethylation of the MSH2 gene promoter was only detected in 14 samples and only at a low level with no correlation to gene expression. MSH2 gene expression was not a prognostic factor for overall survival in univariate or multivariate analysis. The gene expression of MSH2 is a potential quantitative marker ready for further clinical validation.

Integrating biomarkers in clinical trials

Biomarkers have a growing role in clinical trials. With the advent of the targeted therapy era, molecular biomarkers in particular are becoming increasingly important within both clinical research and clinical practice. This article focuses on biomarkers that anticipate the prognosis of individual patients ('prognostic' biomarkers) and on biomarkers that predict how individual patients will respond to specific treatments ('predictive' biomarkers, also called 'effect modifiers'). Specific Phase II and III clinical trial designs are discussed in detail for their ability to validate the biomarker and/or to establish the effect of an experimental therapy in patient populations defined by the presence or absence of the biomarker. Contemporary biomarker-based clinical trials in oncology are used as examples.

Incorporation of biomarker assessment in novel clinical trial designs: personalizing brain tumor treatments

Advances in molecular genetics have aided the identification of potential biomarkers with significant clinical promise in neurooncology. These advances and the evolution of targeted therapeutics necessitate the development and incorporation of innovative clinical trial designs that can effectively validate and assess the clinical utility of biomarkers. In this article, we review the use and potential of several such designs in neurooncology trials in order to support the development of personalized treatment approaches for brain tumor patients.

An algorithm to discover gene signatures with predictive potential

Background

The advent of global gene expression profiling has generated unprecedented insight into our molecular understanding of cancer, including breast cancer. For example, human breast cancer patients display significant diversity in terms of their survival, recurrence, metastasis as well as response to treatment. These patient outcomes can be predicted by the transcriptional programs of their individual breast tumors. Predictive gene signatures allow us to correctly classify human breast tumors into various risk groups as well as to more accurately target therapy to ensure more durable cancer treatment.

Results

Here we present a novel algorithm to generate gene signatures with predictive potential. The method first classifies the expression intensity for each gene as determined by global gene expression profiling as low, average or high. The matrix containing the classified data for each gene is then used to score the expression of each gene based its individual ability to predict the patient characteristic of interest. Finally, all examined genes are ranked based on their predictive ability and the most highly ranked genes are included in the master gene signature, which is then ready for use as a predictor. This method was used to accurately predict the survival outcomes in a cohort of human breast cancer patients.

Conclusions

We confirmed the capacity of our algorithm to generate gene signatures with bona fide predictive ability. The simplicity of our algorithm will enable biological researchers to quickly generate valuable gene signatures without specialized software or extensive bioinformatics training.

Biomarkers and surrogate end points--the challenge of statistical validation

Biomarkers and surrogate end points have great potential for use in clinical oncology, but their statistical validation presents major challenges, and few biomarkers have been robustly confirmed. Provisional supportive data for prognostic biomarkers, which predict the likely outcome independently of treatment, is possible through small retrospective studies, but it has proved more difficult to achieve robust multi-site validation. Predictive biomarkers, which predict the likely response of patients to specific treatments, require more extensive data for validation, specifically large randomized clinical trials and meta-analysis. Surrogate end points are even more challenging to validate, and require data demonstrating both that the surrogate is prognostic for the true end point independently of treatment, and that the effect of treatment on the surrogate reliably predicts its effect on the true end point. In this Review, we discuss the nature of prognostic and predictive biomarkers and surrogate end points, and examine the statistical techniques and designs required for their validation. In cases where the statistical requirements for validation cannot be rigorously achieved, the biological plausibility of an end point or surrogate might support its adoption. No consensus yet exists on processes or standards for pragmatic evaluation and adoption of biomarkers and surrogate end points in the absence of robust statistical validation.

Genetic polymorphisms in GST genes and survival of colorectal cancer patients treated with chemotherapy

Glutathione S-transferases (GSTs) participate in the detoxification of chemotherapeutic agents. Genetic polymorphisms in GST genes (GSTP1 Ile105Val, copy-number variants of GSTM1 and GSTT1) that lead to diminished enzyme activity have been associated with increased chemotherapeutic treatment benefit in colorectal cancer patients.

AIMS

We assessed the effect of genetic polymorphisms in GST genes on survival in colorectal cancer patients treated with adjuvant/palliative chemotherapy. As GSTs participate in the metabolism of platinum metabolites, we also assessed the association between genetic variants in GST genes and survival of colorectal cancer patients who received treatment with oxaliplatin.

MATERIALS & METHODS

We followed 338 colorectal cancer patients treated with chemotherapy for a median of 36.4 months since treatment start. A total of 65 of the patients received treatment with oxaliplatin. Polymorphisms were genotyped by fluorescence-based melting curve analysis (GSTP1 Ile105Val), a relative quantification method (copy-number variants of GSTM1 and GSTT1), and PCR followed by gel electrophoresis (null/non-null genotypes for GSTM1 and GSTT1). Associations between genotypes and overall survival were assessed using Kaplan-Meier curves and Cox proportional hazards regression.

RESULTS

As hypothesized, GSTM1 copy number variant was inversely associated with survival in colorectal cancer patients treated with chemotherapy. Mortality was significantly reduced in patients with one GSTM1 copy (hazard ratio: 0.45, 95% CI: 0.23-0.90, p = 0.02) and nonsignificantly reduced in those with the null genotype (HR: 0.67, 95% CI: 0.35-1.27, p = 0.22) compared with carriers of two copies. Both GSTP1 genotype and GSTT1 genotype were not associated with survival.

CONCLUSION

This is the first study to provide suggestive evidence for an effect of copy-number variation of GSTM1 on survival in colorectal cancer patients who received chemotherapy. Large studies are warranted to establish the impact of GST genotypes on treatment outcome in colorectal cancer patients.

[Prognostic and predictive factors of invasive breast cancer: update 2009]

Prognostic factors supply information on the course of a disease (recurrence-free and total survival) and are independent of the therapy. The most important prognostic factors are lymph node status, tumor diameter and histological differentiation stage, lymph and blood vessel invasion as well as staging, factors which can all be determined by pathologists. The Nottingham prognostic index (NPI) combines the strongest prognostic factors and according to study results is a suitable model for prognosis of breast cancer. Predictive factors give prior information on the probability of the response of a tumor to a defined therapy and include hormone receptor status, the invasion marker uPA/PAI-1, detection of isolated tumor cells, a residual tumor and the histological resection border.Prognostic or predictive factors are clinically relevant when therapy decisions are made possible by their recognition, which lead to an improvement in the total survival, recurrence-free survival or quality of life. The international consensus recommendation of St. Gallen 2007 requires the following as a basis for risk-adapted therapy decisions: tumor size, stage, age, nodal status, hormone receptor status and Her2 overexpression or amplification status.