Clinical Trial Designs for Predictive Marker Validation in Cancer Treatment Trials

OMICS-based personalized oncology: if it is worth doing, it is worth doing well!

The era of Personalized Medicine implies getting the right treatment to the right patient at the right schedule and dose at the right time. Tumor biomarker tests are keys to accomplishing this goal successfully. However, much of the translational research regarding tumor biomarker tests has been haphazard, often using data and specimen sets of convenience and ignoring many of the principles of the scientific method. In papers published simultaneously in BMC Medicine and Nature, McShane and colleagues have proposed a checklist of criteria that should be followed by investigators planning to conduct prospective clinical trials directed towards generating high levels of evidence to demonstrate whether a tumor biomarker test has clinical utility for a specific context. These criteria were generated in response to a roadmap reported by a committee convened by the U.S. Institute of Medicine for generation of omics-based biomarker tests. Taken together with several other initiatives to increase the rigor of tumor biomarker research, these criteria will increase the perception of value for tumor biomarker test research and application in the clinic. Please see related article: http://www.biomedcentral.com/1741-7015/11/220.

Criteria for the use of omics-based predictors in clinical trials: explanation and elaboration

High-throughput 'omics' technologies that generate molecular profiles for biospecimens have been extensively used in preclinical studies to reveal molecular subtypes and elucidate the biological mechanisms of disease, and in retrospective studies on clinical specimens to develop mathematical models to predict clinical endpoints. Nevertheless, the translation of these technologies into clinical tests that are useful for guiding management decisions for patients has been relatively slow. It can be difficult to determine when the body of evidence for an omics-based test is sufficiently comprehensive and reliable to support claims that it is ready for clinical use, or even that it is ready for definitive evaluation in a clinical trial in which it may be used to direct patient therapy. Reasons for this difficulty include the exploratory and retrospective nature of many of these studies, the complexity of these assays and their application to clinical specimens, and the many potential pitfalls inherent in the development of mathematical predictor models from the very high-dimensional data generated by these omics technologies. Here we present a checklist of criteria to consider when evaluating the body of evidence supporting the clinical use of a predictor to guide patient therapy. Included are issues pertaining to specimen and assay requirements, the soundness of the process for developing predictor models, expectations regarding clinical study design and conduct, and attention to regulatory, ethical, and legal issues. The proposed checklist should serve as a useful guide to investigators preparing proposals for studies involving the use of omics-based tests. The US National Cancer Institute plans to refer to these guidelines for review of proposals for studies involving omics tests, and it is hoped that other sponsors will adopt the checklist as well.

Run-in phase III trial design with pharmacodynamics predictive biomarkers

BACKGROUND

Developments in biotechnology have stimulated the use of predictive biomarkers to identify patients who are likely to benefit from a targeted therapy. Several randomized phase III designs have been introduced for development of a targeted therapy using a diagnostic test. Most such designs require biomarkers measured before treatment. In many cases, it has been very difficult to identify such biomarkers. Promising candidate biomarkers can sometimes be effectively measured after a short run-in period on the new treatment.

METHODS

We introduce a new design for phase III trials with a candidate predictive pharmacodynamic biomarker measured after a short run-in period. Depending on the therapy and the biomarker performance, the trial would either randomize all patients but perform a separate analysis on the biomarker-positive patients or only randomize marker-positive patients after the run-in period. We evaluate the proposed design compared with the conventional phase III design and discuss how to design a run-in trial based on phase II studies.

RESULTS

The proposed design achieves a major sample size reduction compared with the conventional randomized phase III design in many cases when the biomarker has good sensitivity (≥0.7) and specificity (≥0.7). This requires that the biomarker be measured accurately and be indicative of drug activity. However, the proposed design loses some of its advantage when the proportion of potential responders is large (>50%) or the effect on survival from run-in period is substantial.

CONCLUSIONS

Incorporating a pharmacodynamic biomarker requires careful consideration but can expand the capacity of clinical trials to personalize treatment decisions and enhance therapeutics development.

Development of omics-based clinical tests for prognosis and therapy selection: the challenge of achieving statistical robustness and clinical utility

BACKGROUND

Many articles have been published in biomedical journals reporting on the development of prognostic and therapy-guiding biomarkers or predictors developed from high-dimensional data generated by omics technologies. Few of these tests have advanced to routine clinical use.

PURPOSE

We discuss statistical issues in the development and evaluation of prognostic and therapy-guiding biomarkers and omics-based tests.

METHODS

Concepts relevant to the development and evaluation of prognostic and therapy-guiding clinical tests are illustrated through discussion and examples. Some differences between statistical approaches for test evaluation and therapy evaluation are explained. The additional complexities introduced in the evaluation of omics-based tests are highlighted.

RESULTS

Distinctions are made between clinical validity of a test and clinical utility. To establish clinical utility for prognostic tests, it is explained why absolute risk should be evaluated in addition to relative risk measures. The critical role of an appropriate control group is emphasized for evaluation of therapy-guiding tests. Common pitfalls in the development and evaluation of tests generated from high-dimensional omics data such as model overfitting and inappropriate methods for test performance evaluation are explained, and proper approaches are suggested.

LIMITATIONS

The cited references do not comprise an exhaustive list of useful references on this topic, and a systematic review of the literature was not performed. Instead, a few key points were highlighted and illustrated with examples drawn from the oncology literature.

CONCLUSIONS

Approaches for the development and statistical evaluation of clinical tests useful for predicting prognosis and selecting therapy differ from standard approaches for therapy evaluation. Proper evaluation requires an understanding of the clinical setting and what information is likely to influence clinical decisions. Specialized expertise relevant to building mathematical predictor models from high-dimensional data is helpful to avoid common pitfalls in the development and evaluation of omics-based tests.

Statistical issues in the validation of prognostic, predictive, and surrogate biomarkers

BACKGROUND

Biomarkers have many distinct purposes, and depending on their intended use, the validation process varies substantially.

PURPOSE

The goal of this article is to provide an introduction to the topic of biomarkers, and then to discuss three specific types of biomarkers, namely, prognostic, predictive, and surrogate.

RESULTS

A principle challenge for biomarker validation from a statistical perspective is the issue of multiplicity. In general, the solution to this multiplicity challenge is well known to statisticians: pre-specification and replication. Critical requirements for prognostic marker validation include uniform treatment, complete follow-up, unbiased case selection, and complete ascertainment of the many possible confounders that exist in the context of an observational sample. In the case of predictive biomarker validation, observational data are clearly inadequate and randomized controlled trials are mandatory. Within the context of randomization, strategies for predictive marker validation can be grouped into two categories: retrospective versus prospective validation. The critical validation criteria for a surrogate endpoint is to ensure that if a trial uses a surrogate endpoint, the trial will result in the same inferences as if the trial had observed the true endpoint. The field of surrogate endpoint validation has now moved to the multi-trial or meta-analytic setting as the preferred method.

CONCLUSIONS

Biomarkers are a highly active research area. For all biomarker developmental and validation studies, the importance of fundamental statistical concepts remains the following: pre-specification of hypotheses, randomization, and replication. Further statistical methodology research in this area is clearly needed as we move forward.

Breast cancer in women at high risk: the role of rapid genetic testing for BRCA1 and -2 mutations and the consequences for treatment strategies

Specific clinical questions rise when patients, who are diagnosed with breast cancer, are at risk of carrying a mutation in BRCA1 and -2 gene due to a strong family history or young age at diagnosis. These questions concern topics such as 1. Timing of genetic counseling and testing, 2. Choices to be made for BRCA1 or -2 mutation carriers in local treatment, contralateral treatment, (neo)adjuvant systemic therapy, and 3. The psychological effects of rapid testing. The knowledge of the genetic status might have several advantages for the patient in treatment planning, such as the choice whether or not to undergo mastectomy and/or prophylactic contralateral mastectomy. The increased risk of developing a second breast cancer in the ipsilateral breast in mutation carriers, is only slightly higher after primary cancer treatment, than in the general population. Prophylactic contralateral mastectomy provides a substantial reduction of contralateral breast cancer, although only a small breast cancer specific survival benefit. Patients should be enrolled in clinical trials to investigate (neo)-adjuvant drug regimens, that based on preclinical and early clinical evidence might be targeting the homologous recombination defect, such as platinum compounds and PARP inhibitors. If rapid testing is performed, the patient can make a well-balanced decision. Although rapid genetic counseling and testing might cause some distress, most women reported this approach to be worthwhile. In this review the literature regarding these topics is evaluated. Answers and suggestions, useful in clinical practice are discussed.

A prospective study evaluating the clinical relevance of a chemoresponse assay for treatment of patients with persistent or recurrent ovarian cancer

OBJECTIVE

Use of in vitro chemoresponse assays for informing effective treatment selection is a compelling clinical question and a topic of debate among oncologists. A prospective study was conducted evaluating the use of a chemoresponse assay in recurrent ovarian cancer patients.

METHODS

Women with persistent or recurrent ovarian cancer were enrolled under an IRB-approved protocol, and fresh tissue samples were collected for chemoresponse testing. Patients were treated with one of 15 protocol-designated treatments empirically selected by the oncologist, blinded to the assay results. Each treatment was classified by the assay as: sensitive (S), intermediate (I), or resistant (R). Patients were prospectively monitored for progression-free survival (PFS) and overall survival (OS). Associations of assay response for the physician-selected treatment with PFS and OS were analyzed.

RESULTS

A total of 262 evaluable patients were enrolled. Patients treated with an assay-sensitive regimen demonstrated significantly improved PFS and OS while there was no difference in clinical outcomes between I and R groups. Median PFS was 8.8 months for S vs. 5.9 months for I+R (hazard ratio [HR]=0.67, p=0.009). The association with assay response was consistent in both platinum-sensitive and platinum-resistant tumors (HR: 0.71 vs. 0.66) and was independent of other covariates in multivariate analysis (HR=0.66, p=0.020). A statistically significant14-month improvement in mean OS (37.5 months for S vs. 23.9 months for I+R, HR=0.61, p=0.010) was demonstrated.

CONCLUSIONS

This prospective study demonstrated improved PFS and OS for patients with either platinum-sensitive or platinum-resistant recurrent ovarian cancer treated with assay-sensitive agents.

Prospective validation of candidate SNPs of VEGF/VEGFR pathway in metastatic colorectal cancer patients treated with first-line FOLFIRI plus bevacizumab

PURPOSE

The potential impact of different SNPs of VEGF/VEGFR pathway on the clinical outcome of mCRC patients receiving bev-containing regimens has been investigated in retrospective experiences with contrasting results. We previously reported the association of VEGFA rs833061 C/T variants with PFS in metastatic colorectal cancer patients treated with first-line FOLFIRI plus bevacizumab. The primary objective of this work was to prospectively validate that retrospective finding. A confirmatory analysis of other SNPs of VEGF/VEGFR pathway genes was included.

EXPERIMENTAL DESIGN

To detect a HR for PFS of 1.7 for VEGFA rs833061 T/T compared to C- variants in metastatic colorectal cancer patients treated with first-line FOLFIRI plus bevacizumab, setting two-sided α = 0.05 and β = 0.20, 199 events were required. VEGFA rs699946 A/G, rs699947 A/C, VEGFR1 rs9582036 A/C and rs7993418 A/G, VEGFR2 rs11133360 C/T, rs12505758 C/T and rs2305948 C/T and EPAS1 rs4145836 A/G were also tested. Germ-line DNA was extracted from peripheral blood. SNPs were analyzed by PCR and sequencing.

RESULTS

Four-hundred-twenty-four pts were included. At the univariate analysis, no differences according to VEGFA rs833061 C/T variants were observed in PFS (p = 0.38) or OS (p = 0.95). Among analyzed SNPs, only VEGFR2 rs12505758 C- variants, compared to T/T, were associated to shorter PFS (HR: 1.36 [1.05-1.75], p = 0.015, dominant genetic model) and OS, with a trend toward significance (HR: 1.34 [0.95-1.88], p = 0.088). In the multivariate model, this association retained significance (HR: 1.405 [1.082-1.825], p = 0.012) in PFS, that was lost by applying multiple testing correction (p = 0.14).

CONCLUSION

This prospective experience failed to validate the hypothesized predictive impact of VEGFA rs833061 variants. Retrospective findings on different candidate SNPs were not confirmed. Only VEGFR2 rs12505758 variants, whose prognostic and not predictive impact was previously reported, correlated with PFS. Given the complexity of angiogenesis, it is rather unlike that a single germ-line SNP might be a good predictor of benefit from bevacizumab.

Impact of bioinformatic procedures in the development and translation of high-throughput molecular classifiers in oncology

The progressive introduction of high-throughput molecular techniques in the clinic allows for the extensive and systematic exploration of multiple biologic layers of tumors. Molecular profiles and classifiers generated from these assays represent the foundation of what the National Academy describes as the future of "precision medicine". However, the analysis of such complex data requires the implementation of sophisticated bioinformatic and statistical procedures. It is critical that oncology practitioners be aware of the advantages and limitations of the methods used to generate classifiers to usher them into the clinic. This article uses publicly available expression data from patients with non-small cell lung cancer to first illustrate the challenges of experimental design and preprocessing of data before clinical application and highlights the challenges of high-dimensional statistical analysis. It provides a roadmap for the translation of such classifiers to clinical practice and makes key recommendations for good practice.

Tumor prognostic factors and the challenge of developing predictive factors

Histopathologic classification has been widely used to type and grade primary brain tumors. However, the diverse behavior of primary brain tumors has made prognostic determinations based purely on clinical and histopathologic variables difficult. Recent advances in the molecular genetics of brain tumors have helped to explain the witnessed heterogeneity regarding response to treatment, time to progression, and overall survival. Additionally, there has been interest in identifying predictive factors to help direct patients to therapeutic interventions specific to their tumor and patient biology. Further identification of both prognostic and predictive biomarkers will make possible better patient stratification and individualization of treatment.

Incorporation of prognostic and predictive factors into glioma clinical trials

Treatment of brain tumors is increasingly informed by biomarkers that predict patient prognosis and response to therapy. While this progress represents a great opportunity for the field of neuro-oncology, it also presents significant challenges. Biomarkers are not straightforward to identify, and previously used clinical trial paradigms are poorly suited to the task of identifying treatments effective only in selected subsets of patients. Unless investigators adapt new tools and procedures that better account for the biological diversity of gliomas, future clinical trials run the dual risk of missing important treatment effects and exposing patients to interventions destined to prove ineffective for their tumors. In this article, we will review the progress made in the past decade with respect to biomarkers in neuro-oncology, address barriers to ongoing progress, and discuss clinical trial designs that may prove useful in moving neuro-oncology fully into the era of personalized medicine.

Platform comparisons for identification of breast cancers with a BRCA-like copy number profile

Previously, we employed bacterial artificial chromosome (BAC) array comparative genomic hybridization (aCGH) profiles from BRCA1 and -2 mutation carriers and sporadic tumours to construct classifiers that identify tumour samples most likely to harbour BRCA1 and -2 mutations, designated 'BRCA1 and -2-like' tumours, respectively. The classifiers are used in clinical genetics to evaluate unclassified variants, and patients for which no good quality germline DNA is available. Furthermore, we have shown that breast cancer patients with BRCA-like tumour aCGH profiles benefit substantially from platinum-based chemotherapy, potentially due to their inability to repair DNA double strand breaks (DSB), providing a further important clinical application for the classifiers. The BAC array technology has been replaced with oligonucleotide arrays. To continue clinical use of existing classifiers, we mapped oligonucleotide aCGH data to the BAC domain, such that the oligonucleotide profiles can be employed as in the BAC classifier. We demonstrate that segmented profiles derived from oligonucleotide aCGH show high correlation with BAC aCGH profiles. Furthermore, we trained a support vector machine score to objectify aCGH profile quality. Using the mapped oligonucleotide aCGH data, we show equivalence in classification of biologically relevant cases between BAC and oligonucleotide data. Furthermore, the predicted benefit of DSB inducing chemotherapy due to a homologous recombination defect is retained. We conclude that oligonucleotide aCGH data can be mapped to and used in the previously developed and validated BAC aCGH classifiers. Our findings suggest that it is possible to map copy number data from any other technology in a similar way.

Companion biomarkers: paving the pathway to personalized treatment for cancer

BACKGROUND

Companion biomarkers are biomarkers that are used in combination with specific therapies and that prospectively help predict likely response or severe toxicity. In this article we review the role of companion biomarkers in guiding treatment in patients with cancer.

CONTENT

In addition to the established companion biomarkers such as estrogen receptors and HER2 (human epidermal growth factor receptor 2) in breast cancer, several new companion biomarkers have become available in recent years. These include v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog (KRAS) mutations for the selection of patients with advanced colorectal cancer who are unlikely to benefit from anti-epidermal growth factor receptor antibodies (cetuximab or panitumumab), epidermal growth factor receptor (EGFR) mutations for selecting patients with advanced non-small cell lung cancer (NSCLC) for treatment with tyrosine kinase inhibitors (gefitinib or erlotinib), v-raf murine sarcoma viral oncogene homolog B1 (BRAF) mutations for selecting patients with advanced melanoma for treatment with anti-BRAF agents (vemurafenib and dabrafenib), and anaplastic lymphoma receptor tyrosine kinase (ALK) translocations for identifying patients with NSCLC likely to benefit from crizotinib.

SUMMARY

The availability of companion biomarkers should improve drug efficacy, decrease toxicity, and lead to a more individualized approach to cancer treatment.

Genomic biomarkers for personalized medicine: development and validation in clinical studies

The establishment of high-throughput technologies has brought substantial advances to our understanding of the biology of many diseases at the molecular level and increasing expectations on the development of innovative molecularly targeted treatments and molecular biomarkers or diagnostic tests in the context of clinical studies. In this review article, we position the two critical statistical analyses of high-dimensional genomic data, gene screening and prediction, in the framework of development and validation of genomic biomarkers or signatures, through taking into consideration the possible different strategies for developing genomic signatures. A wide variety of biomarker-based clinical trial designs to assess clinical utility of a biomarker or a new treatment with a companion biomarker are also discussed.

Feasibility and safety of sequential research-related tumor core biopsies in clinical trials

BACKGROUND

There has been increasing interest in serial research biopsies in studies of targeted therapies. Definition of patient characteristics and optimal target tissue for safe research tumor biopsy in the era of antiangiogenic and targeted agents is needed.

METHODS

This institutional review board-approved, retrospective study included chart and interventional radiology case review from 6 phase 1/2 studies at the National Cancer Institute.

RESULTS

One hundred forty-two of 150 protocol patients who were approached gave consent for research biopsies. Patients' median age was 56 years (range, 27-78 years), their median body mass index was 25.8 kg/m(2) (range, 14.4-46.2 kg/m(2) ), they had an Eastern Cooperative Oncology Group performance status of 0 or 1, and they had normal end-organ function. Baseline biopsies were collected from 138 of 142 patients (97%), and paired specimens were collected from 96 (70%). Most patients had metastatic gynecologic cancers (85%), and 78% had target disease below the diaphragm with a median size of 2.7 cm (range, 1-14.5 cm). Protocol therapies included kinase inhibitors (35%), angiogenesis inhibitors (54%), and olaparib/carboplatin (11%); therapy was not interrupted for biopsies. All adverse events were uncomplicated and were observed in 4 patients (liver subcapsular hematoma in 1 patient, vasovagal syncope in 2 patients, and pneumothorax in 1 patient). The complication rate in obese patients was similar to that in nonobese patients (3 of 108 patients vs 1 of 34 patients, respectively). Sixty-seven patients (48%) were receiving bevacizumab at the time of subsequent biopsies. The complication rate was not different between patients who were and were not receiving bevacizumab (3 of 67 patients vs 1 of 71 patients, respectively). Ninety-five percent of biopsies yielded useable material.

CONCLUSIONS

Serial percutaneous core-needle biopsies can be obtained safely and yield material applicable for multiple translational applications. Obesity and/or concomitant antiangiogenic therapy and depth of disease did not increase the risk or preclude the successful acquisition of useful tissue.

Enzalutamide for the treatment of prostate cancer

INTRODUCTION

The FDA approval of docetaxel for metastatic castration-resistant prostate cancer (mCRPC) in 2005 marked a major milestone, as it was the first approved agent for this disease that demonstrated a survival advantage in Phase III assessment of this disease. Since 2009, several other agents have been approved by FDA, including sipuleucel-T, abiraterone, cabazitaxel and enzalutamide . Enzalutamide, a potent antiandrogen that blocks nuclear translocation of the androgen receptor (AR), is the most recently approved of these agents.

AREAS COVERED

The clinical development of enzalutamide is discussed, with attention given as to how this agent will most appropriately be used among a growing list of agents for mCRPC. A MEDLINE search was conducted to identify all relevant published datasets pertaining to the drug. In addition, relevant ASCO and ESMO abstracts were searched.

EXPERT OPINION

The current role and sequencing of enzalutamide may change drastically based on studies such as PREVAIL (a Phase III pre-chemotherapy assessment of enzalutamide) and planned studies to assess relevant combinations (i.e., enzalutamide with abiraterone). Outside of clinical efficacy, issues such as drug cost may ultimately dictate our utilization of agents such as enzalutamide for mCPRC. Although the development of biomarkers to guide therapy for mCRPC is ideal, there are inherent challenges in establishing biomarker-driven treatment.

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.

Designs and adaptive analysis plans for pivotal clinical trials of therapeutics and companion diagnostics

BACKGROUND

Developments in genomics and biotechnology provide unprecedented opportunities for the development of effective therapeutics and companion diagnostics for matching the right drug to the right patient. Effective co-development involves many new challenges with increased opportunity for success as well as delay and failure.

OBJECTIVE

Clinical trial designs and adaptive analysis plans for the prospective design of pivotal trials of new therapeutics and companion diagnostics are reviewed.

CONCLUSIONS

Effective co-development requires careful prospective planning of the design and analysis strategy for pivotal clinical trials. Randomized clinical trials continue to be important for evaluating the effectiveness of new treatments, but the target populations for analysis should be prospectively specified based on the companion diagnostic. Post hoc analyses of traditionally designed randomized clinical trials are often deeply problematic. Clear separation is generally required of the data used for developing the diagnostic test, including their threshold of positivity, from the data used for evaluating treatment effectiveness in subsets determined by the test. Adaptive analysis can be used to provide flexibility to the analysis but the use of such methods requires careful planning and prospective definition in order to assure that the pivotal trial adequately limits the chance of erroneous conclusions.

Re-overcoming barriers in translating biomarkers to clinical practice

IMPORTANCE OF THE FIELD

Recently, there has been growing evidence for the concept of personalized medicine as the implementation of genomic and molecular information in the delivery of healthcare. In parallel, the identification of biomarkers has become of enormous significance as a prerequisite for individualized intervention regimens.

AREAS COVERED IN THIS REVIEW

Biomarkers are developed to improve prevention, diagnosis or therapeutic outcome of a given disease. Although each application reveals distinct developmental strategies, evidence-based approval of new biomarkers is important for the success of new drugs, diagnostic tests or recommendations in preventive medicine. Current hurdles to bringing biomarkers into clinical practice are reviewed, thereby focusing on adequate approaches to overcome these limitations in the future.

WHAT THE READER WILL GAIN

The reader will get an introduction to strategies resolving actual barriers in clinical biomarker development.

TAKE HOME MESSAGE

The identification of evidence-based biomarkers is crucial for the success of individualized therapeutic approaches. Developmental strategies have to be adapted to clinical need, thereby focusing on biomarker validation in clinical settings as well as on the establishment of standardized biomarker test systems for routine application. Consortia have been established bringing together representatives of government, academia and industry to improve future biomarker development.

A 12-gene set predicts survival benefits from adjuvant chemotherapy in non-small cell lung cancer patients

PURPOSE

Prospectively identifying who will benefit from adjuvant chemotherapy (ACT) would improve clinical decisions for non-small cell lung cancer (NSCLC) patients. In this study, we aim to develop and validate a functional gene set that predicts the clinical benefits of ACT in NSCLC.

EXPERIMENTAL DESIGN

An 18-hub-gene prognosis signature was developed through a systems biology approach, and its prognostic value was evaluated in six independent cohorts. The 18-hub-gene set was then integrated with genome-wide functional (RNAi) data and genetic aberration data to derive a 12-gene predictive signature for ACT benefits in NSCLC.

RESULTS

Using a cohort of 442 stage I to III NSCLC patients who underwent surgical resection, we identified an 18-hub-gene set that robustly predicted the prognosis of patients with adenocarcinoma in all validation datasets across four microarray platforms. The hub genes, identified through a purely data-driven approach, have significant biological implications in tumor pathogenesis, including NKX2-1, Aurora Kinase A, PRC1, CDKN3, MBIP, and RRM2. The 12-gene predictive signature was successfully validated in two independent datasets (n = 90 and 176). The predicted benefit group showed significant improvement in survival after ACT (UT Lung SPORE data: HR = 0.34, P = 0.017; JBR.10 clinical trial data: HR = 0.36, P = 0.038), whereas the predicted nonbenefit group showed no survival benefit for 2 datasets (HR = 0.80, P = 0.70; HR = 0.91, P = 0.82).

CONCLUSIONS

This is the first study to integrate genetic aberration, genome-wide RNAi data, and mRNA expression data to identify a functional gene set that predicts which resectable patients with non-small cell lung cancer will have a survival benefit with ACT.

Recent development on statistical methods for personalized medicine discovery

It is well documented that patients can show significant heterogeneous responses to treatments so the best treatment strategies may require adaptation over individuals and time. Recently, a number of new statistical methods have been developed to tackle the important problem of estimating personalized treatment rules using single-stage or multiple-stage clinical data. In this paper, we provide an overview of these methods and list a number of challenges.

Pulmonary hypertension: biomarkers

Physicians look to biomarkers to inform the management of pulmonary hypertension (PH) at all stages, from assessing susceptibility through screening, diagnosis, and risk stratification to drug selection and monitoring. PH is a heterogeneous disorder and currently there are no accepted blood biomarkers specific to any manifestation of the condition. Brain natriuretic peptide and its N-terminal peptide have been most widely studied. Other candidate prognostic biomarkers in patients with pulmonary arterial hypertension (PAH) include growth and differentiation factor-15, red cell distribution width, uric acid, creatinine, inflammatory markers such as interleukin-6, angiopoietins, and microRNAs. Combining the measurement of biomarkers reflecting different components of the pathology with other modalities may enable better molecular characterisation of PH subtypes and permit improved targeting of therapeutic strategies and disease monitoring.

Efficiency of study designs in diagnostic randomized clinical trials

From the patients' management perspective, a good diagnostic test should contribute to both reflecting the true disease status and improving clinical outcomes. The diagnostic randomized clinical trial is designed to combine both diagnostic tests and therapeutic interventions. Evaluation of diagnostic tests is carried out with therapeutic outcomes as the primary endpoint rather than test accuracy. We lay out the probability framework for evaluating such trials. We compare two commonly referred designs-the two-arm design and the paired design-in a formal statistical hypothesis testing setup and identify the causal connection between the two tests. The paired design is shown to be more efficient than the two-arm design. The efficiency gains vary depending on the discordant rates of test results. We derive sample size formulas for both binary and continuous endpoints. We derive estimation of important quantities under the paired design and also conduct simulation studies to verify the theoretical results. We illustrate the method with an example of designing a randomized study on preoperative staging of bladder cancer.

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.

Low ERK phosphorylation in cancer-associated fibroblasts is associated with tamoxifen resistance in pre-menopausal breast cancer

Purpose

The aim of this study was to evaluate ERK phosphorylation as a stromal biomarker for breast cancer prognosis and tamoxifen treatment prediction within a randomized tamoxifen trial.

Patients and Methods

Tissue microarrays of two breast cancer cohorts including in total 743 invasive breast cancer samples were analyzed for ERK phosphorylation (pERK) and smooth muscle actin-alpha expression (SMAα) in cancer-associated fibroblasts (CAFs) and links to clinico-pathological data and treatment-predictive values were delineated.

Results

By analyzing a unique randomized tamoxifen trial including breast cancer patients receiving no adjuvant treatment we show for the first time that patients low in ERK phosphorylation in CAFs did not respond to tamoxifen treatment despite having estrogen-receptor alpha (ERα-positive tumors compared to patients with high pERK levels in CAFs (P = 0.015, multivariate Cox regression interaction analysis). In both clinical materials we further show a significant association between pERK and SMAα, a characteristic marker for activated fibroblasts. SMAα expression however was not linked to treatment-predictive information but instead had prognostic qualities.

Conclusion

The data suggests that the presence of a subpopulation of CAFs, defined by minimal activated ERK signaling, is linked to an impaired tamoxifen response. Thus, this report illustrates the importance of the stroma for monitoring treatment effects in pre-menopausal breast cancer.

Assessing treatment-selection markers using a potential outcomes framework

Treatment-selection markers are biological molecules or patient characteristics associated with one's response to treatment. They can be used to predict treatment effects for individual subjects and subsequently help deliver treatment to those most likely to benefit from it. Statistical tools are needed to evaluate a marker's capacity to help with treatment selection. The commonly adopted criterion for a good treatment-selection marker has been the interaction between marker and treatment. While a strong interaction is important, it is, however, not sufficient for good marker performance. In this article, we develop novel measures for assessing a continuous treatment-selection marker, based on a potential outcomes framework. Under a set of assumptions, we derive the optimal decision rule based on the marker to classify individuals according to treatment benefit, and characterize the marker's performance using the corresponding classification accuracy as well as the overall distribution of the classifier. We develop a constrained maximum-likelihood method for estimation and testing in a randomized trial setting. Simulation studies are conducted to demonstrate the performance of our methods. Finally, we illustrate the methods using an HIV vaccine trial where we explore the value of the level of preexisting immunity to adenovirus serotype 5 for predicting a vaccine-induced increase in the risk of HIV acquisition.

Biomarker-based targeting of the androgen-androgen receptor axis in advanced prostate cancer

Recent therapeutic advances for managing advanced prostate cancer include the successful targeting of the androgen-AR axis with several new drugs in castrate resistant prostate cancer including abiraterone acetate and enzalutamide (MDV3100). This translational progress from “bench to bed-side” has resulted in an enlarging repertoire of novel and traditional drug choices now available for use in advanced prostate cancer therapeutics, which has had a positive clinical impact in prolonging longevity and quality of life of advanced prostate cancer patients. In order to further the clinical utility of these drugs, development of predictive biomarkers guiding individual therapeutic choices remains an ongoing challenge. This paper will summarize the potential in developing predictive biomarkers based on the pathophysiology of the androgen-AR axis in tumor tissue from patients with advanced prostate cancer as well as inherited variation in the patient's genome. Specific examples of rational clinical trial designs incorporating potential predictive biomarkers from these pathways will illustrate several aspects of pharmacogenetic and pharmacogenomic predictive biomarker development in advanced prostate cancer therapeutics.

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.

A 2-stage phase II design with direct assignment option in stage II for initial marker validation

Biomarkers are critical to targeted therapies, as they may identify patients more likely to benefit from a treatment. Several prospective designs for biomarker-directed therapy have been previously proposed, differing primarily in the study population, randomization scheme, or both. Recognizing the need for randomization, yet acknowledging the possibility of promising but inconclusive results after a stage I cohort of randomized patients, we propose a 2-stage phase II design on marker-positive patients that allows for direct assignment in a stage II cohort. In stage I, marker-positive patients are equally randomized to receive experimental treatment or control. Stage II has the option to adopt "direct assignment" whereby all patients receive experimental treatment. Through simulation, we studied the power and type I error rate of our design compared with a balanced randomized two-stage design, and conducted sensitivity analyses to study the effect of timing of stage I analysis, population shift effects, and unbalanced randomization. Our proposed design has minimal loss in power (<1.8%) and increased type I error rate (<2.1%) compared with a balanced randomized design. The maximum increase in type I error rate in the presence of a population shift was between 3.1% and 5%, and the loss in power across possible timings of stage I analysis was less than 1.2%. Our proposed design has desirable statistical properties with potential appeal in practice. The direct assignment option, if adopted, provides for an "extended confirmation phase" as an alternative to stopping the trial early for evidence of efficacy in stage I.

Design of a phase III clinical trial with prospective biomarker validation: SWOG S0819

The role of cetuximab in the treatment of advanced non-small cell lung cancer (NSCLC) is currently unclear. The molecular target of cetuximab, epidermal growth factor receptor (EGFR), as measured by FISH, has shown potential as a predictive biomarker for cetuximab efficacy in NSCLC. SWOG S0819 is a phase III trial evaluating both the value of cetuximab in this setting and EGFR FISH as a predictive biomarker. This work describes the decision process for determining the design and interim monitoring plan for S0819. Six possible designs were evaluated in terms of their properties and the hypotheses that can be addressed within the design constraints. A subgroup-focused, multiple-hypothesis design was selected for S0819 that incorporates coprimary endpoints to assess cetuximab in both the overall study population and among EGFR FISH-positive (FISH(+)) patients, with the sample size determined based on evaluation in the EGFR FISH(+) group. The chosen interim monitoring plan specifies interim evaluations of both efficacy and futility in the EGFR FISH(+) group alone. The futility-monitoring plan to determine early stopping in the EGFR FISH-nonpositive group is based on evaluation within the positive group, the entire study population, and the nonpositive group. SWOG S0819 uses a design that addresses both the biomarker-driven and general-efficacy objectives of this study.

Personalized medicine using DNA biomarkers: a review

Biomarkers are of increasing importance for personalized medicine, with applications including diagnosis, prognosis, and selection of targeted therapies. Their use is extremely diverse, ranging from pharmacodynamics to treatment monitoring. Following a concise review of terminology, we provide examples and current applications of three broad categories of biomarkers—DNA biomarkers, DNA tumor biomarkers, and other general biomarkers. We outline clinical trial phases for identifying and validating diagnostic and prognostic biomarkers. Predictive biomarkers, more generally termed companion diagnostic tests predict treatment response in terms of efficacy and/or safety. We consider suitability of clinical trial designs for predictive biomarkers, including a detailed discussion of validation study designs, with emphasis on interpretation of study results. We specifically discuss the interpretability of treatment effects if a large set of DNA biomarker profiles is available and the number of therapies is identical to the number of different profiles.

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.

Randomized controlled trials on PET: a systematic review of topics, design, and quality

Randomized controlled trials (RCTs) add important information to diagnostic accuracy studies in the evaluation of PET and PET/CT. We evaluated how many RCTs on PET existed, which clinical topics they addressed, and what their design and quality were.

METHODS

We searched MEDLINE, EMBASE, and the Cochrane Central Register of Controlled Trials (Clinical Trials) up to August 2010. We also searched in ClinicalTrials.gov and the International Clinical Trials Registry Platform for ongoing RCTs up to March 2011. Titles and abstracts and full texts were screened independently by 2 reviewers. Study characteristics were extracted with standard extraction sheets for ongoing and published RCTs, and risk of bias was assessed for published ones.

RESULTS

We identified 54 RCTs, 12 of which were published. The main topics in published studies were non-small cell lung cancer and colorectal cancer; only 3 were conducted in nononcologic fields (this trend was similar in ongoing studies, in which the most common topic was Hodgkin disease). The main indications in the oncologic PET studies were staging in published studies and restaging (mostly including an early assessment of treatment response) in ongoing ones. All except 1 of the published studies applied a marker-based strategy design, whereas about 43% (18/42) of ongoing studies use a more efficient design (Enrichment Design or Marker by Treatment Interaction Design).

CONCLUSION

A relatively high number of ongoing RCTs of PET in several oncologic fields are expected to produce robust results over the next few years. For nononcologic topics, further high-quality studies are still needed to ascertain the benefit of this technique for patients. As funding is usually difficult in nondrug topics, alternative concepts of funding, which should also involve the manufacturers of diagnostic devices, but also more efficient study designs, should be applied to bridge the evidence gap on PET in the near future.

Estimating Individualized Treatment Rules Using Outcome Weighted Learning

There is increasing interest in discovering individualized treatment rules for patients who have heterogeneous responses to treatment. In particular, one aims to find an optimal individualized treatment rule which is a deterministic function of patient specific characteristics maximizing expected clinical outcome. In this paper, we first show that estimating such an optimal treatment rule is equivalent to a classification problem where each subject is weighted proportional to his or her clinical outcome. We then propose an outcome weighted learning approach based on the support vector machine framework. We show that the resulting estimator of the treatment rule is consistent. We further obtain a finite sample bound for the difference between the expected outcome using the estimated individualized treatment rule and that of the optimal treatment rule. The performance of the proposed approach is demonstrated via simulation studies and an analysis of chronic depression data.

Guidelines for health technologies: specific guidance for oncology products in Canada

OBJECTIVE

Specific methodological challenges are often encountered during cancer-related economic evaluations. The objective of this study was to provide specific guidance to analysts on the methods for the conduct of high-quality economic evaluations in oncology by building on the Canadian Agency for Drugs and Technologies in Health Guidelines for the Economic Evaluation of Health Technologies (third edition).

METHODS

Fifteen oncologists, health economists, health services researchers, and decision makers from across Canada identified sections in Canadian Agency for Drugs and Technologies in Health guidelines that would benefit from oncology-specific guidance. Fifteen sections of the guidelines were reviewed to determine whether 1) Canadian Agency for Drugs and Technologies in Health guidelines were sufficient for the conduct of oncology economic evaluations without further guidance specific for oncology products or 2) additional guidance was necessary. A scoping review was conducted by using a comprehensive and replicable search to identify relevant literature to inform recommendations. Recommendations were reviewed by representatives of academia, government, and the pharmaceutical industry in an iterative and formal review of the recommendations.

RESULTS

Major adaptations for guidance related to time horizon, effectiveness, modeling, costs, and resources were required. Recommendations around the use of final outcomes over intermediate outcomes to calculate quality-adjusted life-years and life-years gained, the type of evidence, the source of evidence, and the use of time horizon and modeling were made.

CONCLUSIONS

This article summarizes key recommendations for the conduct of economic evaluations in oncology and describes methods required to ensure that economic assessments in oncology are conducted in a standardized manner.

Where now for anti-EGF receptor therapies in colorectal cancer?

Current US FDA-approved monoclonal antibodies targeting the EGF receptor (EGFR) include cetuximab and panitumumab. In this article, we discuss the clinical evidence concerning the use of monoclonal antibodies targeting the EGFR in the setting of advanced colorectal cancer and the emergence of predictive molecular biomarkers. In addition, we also consider the evidence surrounding the evolution of anti-EGFR-resistance mechanisms evoked by targeted anti-EGFR therapy and potential therapeutic strategies that may counteract resistant tumor growth.

Predictive biomarkers in colorectal cancer: usage, validation, and design in clinical trials

As cancer treatment development has shifted its attention to targeted therapies, it is becoming increasingly important to provide tools for selecting the right treatment for an individual patient to achieve optimal clinical benefit. Biomarkers, identified and studied in the process of understanding the nature of the disease at the molecular pathogenesis level, have been increasingly recognized as a critical aspect in more accurate diagnosis, prognosis assessment, and therapeutic targeting. Predictive biomarkers, which can aid treatment decisions, require extensive data for validation. In this article, we discuss the definition, clinical usages, and more extensively the clinical trial designs for the validation of predictive biomarkers. Predictive biomarker validation methods can be broadly grouped into retrospective and prospective designs. Retrospective validation utilizes data from previously conducted prospective randomized controlled trials. Prospective designs include enrichment designs, treatment-by-marker interaction designs, marker-based strategy designs, and adaptive designs. We discuss each design with examples and provide comparisons of the advantages and disadvantages among the different designs. We conclude that the combination of scientific, clinical, statistical, ethical, and practical considerations provides guidance for the choice of the clinical trial design for validation of each proposed predictive biomarker.