Clinical Trial Designs for Predictive Marker Validation in Cancer Treatment Trials

Cancer biomarkers

Biomarkers have many potential applications in oncology, including risk assessment, screening, differential diagnosis, determination of prognosis, prediction of response to treatment, and monitoring of progression of disease. Because of the critical role that biomarkers play at all stages of disease, it is important that they undergo rigorous evaluation, including analytical validation, clinical validation, and assessment of clinical utility, prior to incorporation into routine clinical care. In this review we address key steps in the development of biomarkers, including ways to avoid introducing bias and guidelines to follow when reporting results of biomarker studies.

A two-stage Bayesian design for co-development of new drugs and companion diagnostics

Most new drug development in oncology is based on targeting specific molecules. Genomic profiles and deregulated drug targets vary from patient to patient making new treatments likely to benefit only a subset of patients traditionally grouped in the same clinical trials. Predictive biomarkers are being developed to identify patients who are most likely to benefit from a particular treatment; however, their biological basis is not always conclusive. The inclusion of marker-negative patients in a trial is therefore sometimes necessary for a more informative evaluation of the therapy. In this paper, we present a two-stage Bayesian design that includes both marker-positive and marker-negative patients in a clinical trial. We formulate a family of prior distributions that represent the degree of a priori confidence in the predictive biomarker. To avoid exposing patients to a treatment to which they may not be expected to benefit, we perform an interim analysis that may stop accrual of marker-negative patients or accrual of all patients. We demonstrate with simulations that the design and priors used control type I errors, give adequate power, and enable the early futility analysis of test-negative patients to be based on prior specification on the strength of evidence in the biomarker.

Genomic instability in breast and ovarian cancers: translation into clinical predictive biomarkers

Breast and ovarian cancer are among the most common malignancies diagnosed in women worldwide. Together, they account for the majority of cancer-related deaths in women. These cancer types share a number of features, including their association with hereditary cancer syndromes caused by heterozygous germline mutations in BRCA1 or BRCA2. BRCA-associated breast and ovarian cancers are hallmarked by genomic instability and high sensitivity to DNA double-strand break (DSB) inducing agents due to loss of error-free DSB repair via homologous recombination (HR). Recently, poly(ADP-ribose) polymerase inhibitors, a new class of drugs that selectively target HR-deficient tumor cells, have been shown to be highly active in BRCA-associated breast and ovarian cancers. This finding has renewed interest in hallmarks of HR deficiency and the use of other DSB-inducing agents, such as platinum salts or bifunctional alkylators, in breast and ovarian cancer patients. In this review we discuss the similarities between breast and ovarian cancer, the hallmarks of genomic instability in BRCA-mutated and BRCA-like breast and ovarian cancers, and the efforts to search for predictive markers of HR deficiency in order to individualize therapy in breast and ovarian cancer.

Why Hasn't Genomic Testing Changed the Landscape in Clinical Oncology?

The "omics" revolution produced great optimism that tumor biomarker tests based on high-order analysis of multiple (sometimes thousands) of factors would result in truly personalized oncologic care. Unfortunately, 10 years into the revolution, the promise of omics-based research has not yet been realized. The factors behind the slow progress in omics-based clinical care are many. First, over the last 15 years, there has been a gradual recognition of the importance of conducting tumor biomarker science with the kind of rigor that has traditionally been used for therapeutic research. However, this recognition has only recently been applied widely, and therefore most tumor biomarkers have insufficiently high levels of evidence to determine clinical utility. Second, omics-based research offers its own particular set of concerns, especially in regard to overfitting computational models and false discovery rates. Researchers and clinicians need to understand the importance of analytic validity, and the difference between clinical/biologic validity and clinical utility. The latter is required to introduce a tumor biomarker test of any kind (single analyte or omics-based), and are ideally generated by carefully planned and properly conducted "prospective retrospective" or truly prospective clinical trials. Only carefully planned studies, which take all three of these into account and in which the investigators are aware and recognize the enormous risk of unintended bias and overfitting inherent in omics-based test development, will ultimately result in translation of the exciting new technologies into better care for patients with cancer.

Prediction of post-remission survival in acute myeloid leukaemia: a post-hoc analysis of the AML96 trial

BACKGROUND

The optimum post-remission treatment (PRT) in acute myeloid leukaemia (AML) is still a matter of debate. Consolidation treatments include chemotherapy with high-dose cytarabine, or allogeneic or autologous haemopoietic stem cell transplantation (HSCT). In a post-hoc analysis of the AML96 trial (NCT00180115), our aim was to differentiate groups of patients according to the treatments that would provide them optimum benefit.

METHODS

In the multicentre AML96 trial, 586 patients (aged 15-60 years) with AML--excluding those with t(8;21)--who were in complete remission after double induction treatment were consolidated with allogeneic HSCT, autologous HSCT, or chemotherapy containing high-dose cytarabine in a priority-based and risk-adapted manner. We assessed the association between potentially prognostic variables and overall survival after complete remission by use of a stratified Cox regression analysis. With the significant variables of the resulting model, we developed a PRT score in 452 patients with a complete dataset. This score was then validated by use of data from 407 patients from the AML2003 trial (NCT00180102).

FINDINGS

Age, percentage of CD34-positive blasts, FLT3-ITD mutant-to-wild-type ratio, cytogenetic risk, and de-novo or secondary AML were identified as independent prognostic factors, and included in the PRT score. The PRT score separated patients in AML96 into three groups: favourable (n=190; 3-year survival 68%, 95% CI 60-74), intermediate (n=198; 49%, 42-56), and unfavourable (n=64; 20%, 12-31). All pair-wise comparisons of two of three PRT score groups were significant in the log-rank test (p<0·0001). Similar results were noted when data from AML2003 were used: 3-year survival for the favourable group (n=265) was 69% (62-76), for the intermediate group (n=114) it was 61% (50-71), and for the unfavourable group (n=28) it was 46% (24-65). The overall comparison between the three risk groups resulted in significantly different survival probabilities (p=0·015). We also analysed response to treatment in AML96 in each of the PRT score groups. In the favourable group, patients given allogeneic HSCT (n=60) had higher survival probabilities (82%, 69-89) than did those given chemotherapy (n=56, 55%, 41-67; p=0·0012) or autologous HSCT (n=74, 66%, 54-76; p=0·044). In the intermediate PRT score group, patients given autologous HSCT (n=69) had the best survival probabilities (62%, 50-72) compared with those given chemotherapy (n=72, 41%, 30-52; p=0·0006) or allogeneic HSCT (n=57, 44%, 31-56; p=0·0045).

INTERPRETATION

The PRT score groups could help physicians to tailor treatment for patients with AML and our results lend support to the use of autologous HSCT in patients aged 60 years or younger with an intermediate PRT score.

FUNDING

Deutsche Krebshilfe.

Design of clinical trials for biomarker research in oncology

The developmental pathway from discovery to clinical practice for biomarkers and biomarker-directed therapies is complex. While several issues need careful consideration, two critical issues that surround the validation of biomarkers are the choice of clinical trial design (which is based on the strength of the preliminary evidence and marker prevalence) and the biomarker assay related issues surrounding the marker assessment methods such as the reliability and reproducibility of the assay. This review focuses on trial designs for marker validation, both in the setting of early phase trials for initial validation, as well as in the context of larger definitive trials. Designs for biomarker validation are broadly classified as retrospective (i.e., using data from previously well-conducted, randomized, controlled trials) or prospective (enrichment, allcomers or adaptive). We believe that the systematic evaluation and implementation of these design strategies are essential to accelerate the clinical validation of biomarker-guided therapy, thereby taking us a step closer to the goal of personalized medicine.

Proteomics and biomarkers in clinical trials for drug development

Proteomics allows characterization of protein structure and function, protein-protein interactions, and peptide modifications. It has given us insight into the perturbations of signaling pathways within tumor cells and has improved the discovery of new therapeutic targets and possible indicators of response to and duration of therapy. The discovery, verification, and validation of novel biomarkers are critical in streamlining clinical development of targeted compounds, and directing rational treatments for patients whose tumors are dependent upon select signaling pathways. Studies are now underway in many diseases to examine the immune or inflammatory proteome, vascular proteome, cancer or disease proteome, and other subsets of the specific pathology microenvironment. Successful assay verification and biological validation of such biomarkers will speed development of potential agents to targetable dominant pathways and lead to selection of individuals most likely to benefit. Reconsideration of analytical and clinical trials methods for acquisition, examination, and translation of proteomics data must occur before we march further into future of drug development.

Clinical trials in the era of personalized oncology

The rapid pace of discoveries in tumor biology, imaging technology, and human genetics hold promise for an era of personalized oncology care. The successful development of a handful of new targeted agents has generated much hope and hype about the delivery of safer and more effective new treatments for cancer. The design and conduct of clinical trials has not yet adjusted to a new era of personalized oncology and so we are more in transition to that era than in it. With the development of treatments for breast cancer as a model, we review the approaches to clinical trials and the development of novel therapeutics in the prior era of population oncology, the current transitional era, and the future era of personalized oncology.

Proteomics as a guiding tool for more effective personalized therapy

Ovarian cancer remains the deadliest gynecological malignancy in the Western world and is most often diagnosed at a rarely curable late stage. Examination of protein end points has been employed as an investigative mechanism to guide targeted therapy and to stratify ovarian cancer. Proteomics allows characterization of the proteins and the associated protein and peptide modifications. This has given us insight into the perturbations of signaling pathways within tumor cells and has improved the discovery of new drug targets and possible prognostic indicators of outcome and disease response to therapy. Development of validated assays that survey the genetic and/or proteomic make-up of an individual tumor will add greatly to the histological classification of the tumor and may lead to different treatment approaches tailored to the unique expression pattern of each individual patient. It is anticipated that application of proteomics may bring to reality the clinical adoption of molecular stratification, e.g. not, 'is the gene overexpressed?', but 'is the pathway upregulated?' This will be successful if validated peptide biomarkers are applied for patient selection prospectively and with inclusion of preplanned biological correlates. These events will guide future directions of proteomics as a selector and as a validator and will guide how we integrate proteomics information daily into patient care and into selecting therapy of advanced and recurrent ovarian cancer and other cancers.

Recent progress and clinical importance on pharmacogenetics in cancer therapy

Recent advances have provided unprecedented opportunities to identify prognostic and predictive markers of efficacy of cancer therapy. Genetic markers can be used to exclude patients who will not benefit from therapy, exclude patients at high risk of severe toxicity and adjust dosing. Genomic approaches for marker discovery now include genome-wide association studies and tumor DNA sequencing. The challenge is now to select markers for which there is enough evidence to transition them to the clinic. The hurdles include the inherent low frequency of many of these markers, the lengthy validation process through trials, as well as legislative and economic hurdles. Attempts to answer questions about certain markers more quickly have led to an increased popularity of trials with enrichment design, especially in light of the dramatic phase I results seen in recent months. Personalized medicine in oncology is a step closer to reality.

Statistical considerations for the next generation of clinical trials

In this article, we address some of the statistical issues associated with the next generation of oncology clinical trials. Specifically, we focus on two critical aspects of oncologic clinical trials: study endpoints and study design. In our discussion of study endpoints, we provide an overview of endpoints relevant to each phase of clinical trials (phases I, II, and III) and discuss some of the trends in selecting endpoints in recent years. For phase I designs, the traditional assumption that increasing dose will always lead to increasing efficacy, appropriate for cytotoxic agents, is not applicable to novel therapeutics such as in colorectal carcinoma. We emphasize the role of surrogate endpoints in modern clinical trials. In our discussion of study design, we are particularly interested in the essential role of randomization, and discuss recently developed randomized phase II designs. We consider the role biomarkers play in the design of clinical trials and introduce study designs for biomarker evaluations. Finally, we briefly discuss the application of adaptive designs in clinical trials.

[Individualized medicine - our (lack of) understanding]

A so called individualized or personalized medicine is currently stimulating peculiar attention. The terms promise better, i.e. more successful, medical treatment with fewer side effects for the future. The present article discusses the biologic concept underlying the terms as well as the promises connected with it and places the terms into the broader context of diagnostic methods. It is pointed out that an assessment of methods called individualized medicine can and should be carried out in adherence to the same methodological principles as they apply to the assessment of other diagnostic tests. Even individualized medicine needs to be subjected to the standard evaluation and review procedures of evidence based medicine.

Barrett's esophagus: treatments of adenocarcinomas II

The following topics are explored in this collection of commentaries on treatments of adenocarcinomas related to Barrett's esophagus: the importance of intraoperative frozen sections of the margins for the detection of high dysplasia; the preferable way for sentinel node dissection; the current role of robotic surgery and of video-endoscopic approach; the value of the Siewert's classification of adenocarcinomas; the indications of two-step esophagectomy; the evaluation of pathological complete response; the role of PET scan in staging and response assessment; the role of p53 in the selection of adenocarcinomas patients; chemotherapy regimens for adenocarcinomas; the use of monoclonal antibodies in the control of cell proliferation; he attempt to define a stage-specific strategy, and the possible indications of selective therapy; and changes in mortality rates from esophageal cancer.

Antiangiogenic therapy for patients with recurrent and newly diagnosed malignant gliomas

Malignant gliomas have a poor prognosis despite advances in diagnosis and therapy. Although postoperative temozolomide and radiotherapy improve overall survival in glioblastoma patients, most patients experience a recurrence. The prognosis of recurrent malignant gliomas is dismal, and more effective therapeutic strategies are clearly needed. Antiangiogenesis is currently considered an attractive targeting therapy for malignant gliomas due to its important role in tumor growth. Clinical trials using bevacizumab have been performed for recurrent glioblastoma, and these studies have shown promising response rates along with progression-free survival. Based on the encouraging results, bevacizumab was approved by the FDA for the treatment of recurrent glioblastoma. In addition, bevacizumab has shown to be effective for recurrent anaplastic gliomas. Large phase III studies are currently ongoing to demonstrate the efficacy and safety of the addition of bevacizumab to temozolomide and radiotherapy for newly diagnosed glioblastoma. In contrast, several other antiangiogenic drugs have also been used in clinical trials. However, previous studies have not shown whether antiangiogenesis improves the overall survival of malignant gliomas. Specific severe side effects, difficult assessment of response, and lack of rational predictive markers are challenging problems. Further studies are warranted to establish the optimized antiangiogenesis therapy for malignant gliomas.

Translational application of epigenetic alterations: ovarian cancer as a model

Cancer is a disease initiated and driven by the accumulation and interplay of genetic and epigenetic mutations of genes involved in the regulation of cell growth and signaling. Dysregulation of these genes and pathways in a cell leads to a growth advantage and clonal expansion. The epigenetic alterations involved in the initiation and progression of cancer are DNA methylation and histone modifications which interact to remodel chromatin, as well as RNA interference. These alterations can be used as candidate targets in molecular tests for risk, early detection, prognosis, prediction of response to therapy, and monitoring, as well as new therapeutic targets in cancer. In this review, we discuss the rationale, studies to date, and issues in the translational application of epigenetics using epithelial ovarian cancer as a specific example of all types of cancer.

An aCGH classifier derived from BRCA1-mutated breast cancer and benefit of high-dose platinum-based chemotherapy in HER2-negative breast cancer patients

BACKGROUND

Breast cancer cells deficient for BRCA1 are hypersensitive to agents inducing DNA double-strand breaks (DSB), such as bifunctional alkylators and platinum agents. Earlier, we had developed a comparative genomic hybridisation (CGH) classifier based on BRCA1-mutated breast cancers. We hypothesised that this BRCA1-like(CGH) classifier could also detect loss of function of BRCA1 due to other causes besides mutations and, consequently, might predict sensitivity to DSB-inducing agents.

PATIENTS AND METHODS

We evaluated this classifier in stage III breast cancer patients, who had been randomly assigned between adjuvant high-dose platinum-based (HD-PB) chemotherapy, a DSB-inducing regimen, and conventional anthracycline-based chemotherapy. Additionally, we assessed BRCA1 loss through mutation or promoter methylation and immunohistochemical basal-like status in the triple-negative subgroup (TN subgroup).

RESULTS

We observed greater benefit from HD-PB chemotherapy versus conventional chemotherapy among patients with BRCA1-like(CGH) tumours [41/230 = 18%, multivariate hazard ratio (HR) = 0.12, 95% confidence interval (CI) 0.04-0.43] compared with patients with non-BRCA1-like(CGH) tumours (189/230 = 82%, HR = 0.78, 95% CI 0.50-1.20), with a significant difference (test for interaction P = 0.006). Similar results were obtained for overall survival (P interaction = 0.04) and when analyses were restricted to the TN subgroup. Sixty-three percent (20/32) of assessable BRCA1-like(CGH) tumours harboured either a BRCA1 mutation (n = 8) or BRCA1 methylation (n = 12).

CONCLUSION

BRCA1 loss as assessed by CGH analysis can identify patients with substantially improved outcome after adjuvant DSB-inducing chemotherapy when compared with standard anthracycline-based chemotherapy in our series.

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.

Critical comments for roles of biomarkers in the diagnosis and treatment of cancer

A biomarker is defined as "a characteristic that is objectively measured and evaluated as an indicator of normal biologic processes, pathogenic processes, or pharmacologic/pharmacodynamic responses to a therapeutic intervention". Various assays, including immunohistochemistry, gene constitution such as amplification, mutation, and rearrangement, gene and protein expression analysis such as single gene or protein expression, exhaustive analysis and gene or protein signature and single nucleotide polymorphism have been used to identify biomarkers in recent years. No therapeutic effects have yet been predicted based on the results of such exhaustive gene analysis because of low reproducibility although some correlate with the prognosis of patients. Biomarkers such as HER2 for breast cancer or EGFR mutation for lung cancer and KRAS mutation in colon cancer have contributed to identify a patient population that might show a good and bad treatment response, respectively. On the other hand, other biomarkers such as bcr-abl, c-kit gene mutation and CD20 expression, which are positive for CML, GIST and B cell lymphoma, respectively, have crucial biological significance but have not necessarily been used for practical clinical screening since pathological diagnosis coincide with finding of biomarkers. Hence, much work remains to be done in many areas of biomarker research.

Procalcitonin for triage of patients with respiratory tract symptoms: a case study in the trial design process for approval of a new diagnostic test for lower respiratory tract infections

Symptoms of cough, fever, chest pain, and shortness of breath are common reasons that patients seek medical care, and they can be due to a variety of medical conditions, including lower respiratory tract infection (LRTI). Only a small proportion of these patients will actually have a bacterial etiology, but many will receive antibiotic treatment because physicians cannot readily determine the etiology at the time of presentation. Current diagnostic methodologies are not sensitive or specific enough to reliably distinguish bacterial from viral or noninfectious etiologies. Procalcitonin (PCT) is a marker of host response. PCT serum levels are elevated in patients with bacterial infection, compared with levels in those with viral infections or other inflammatory pulmonary conditions. Studies have suggested that the determination of PCT levels can identify a subset of patients with LRTI symptoms who can safely avoid antibiotic treatment. As with any new test, clinical trials are necessary to demonstrate the safety and efficacy of the test to obtain U.S. Food and Drug Administration clearance. However, in the absence of standard reference methods for comparison that are reliably sensitive and specific, meeting the regulatory requirements for proof of safety and efficacy is a major challenge. Additional challenges include the choice of study design, the definition and determination of end points, and the justification of statistical analysis.

Personalized therapy for urothelial cancer: review of the clinical evidence

Despite a detailed understanding of the molecular aberrations driving the development of urothelial cancers, this knowledge has not translated into advances for the treatment of this disease. Urothelial cancers are chemosensitive, and platinum-based combination chemotherapy remains the standard of care for advanced disease, as well as neoadjuvant and adjuvant therapy for locally advanced disease. However, nearly half of patients who undergo resection of locally advanced urothelial cancer will relapse and eventually develop platinum-resistant disease. Clinical trials of targeted agents against angiogenesis and growth factors, as well as novel chemotheraputics, have generally been unsuccessful in urothelial cancers. Improvements in the theraputic arsenal for urothelial cancer depend upon identification of new targets and strategies to overcome platinum resistance.

Future directions of mammalian target of rapamycin (mTOR) inhibitor therapy in renal cell carcinoma

With an explosion of available treatments for metastatic renal cell carcinoma (mRCC) in recent years, it is important to recognize that approved targeted therapies fall broadly into only two mechanistic categories. The first category, vascular endothelial growth factor (VEGF)-directed therapies, includes sunitinib, pazopanib, sorafenib and bevacizumab. The second category includes inhibitors of the mammalian target of rapamycin (mTOR), namely everolimus and temsirolimus. A pivotal trial of everolimus supports use of the agent in patients with mRCC refractory to VEGF- tyrosine kinase inhibitors (TKI) therapy, while pivotal data for temsirolimus supports use in poor-prognosis patients as first-line therapy. Multiple reviews exist to delineate the laboratory and clinical development of mTOR inhibitors. This paper will outline the future applications of these therapies. It will explore ongoing trials evaluating combinations of mTOR inhibitors with other targeted therapies, along with sequencing strategies and biomarker discovery efforts. The application of mTOR inhibitors in unique populations is also described.

Measuring the performance of markers for guiding treatment decisions

Treatment selection markers, sometimes called predictive markers, are factors that help clinicians select therapies that maximize good outcomes and minimize adverse outcomes for patients. Existing statistical methods for evaluating a treatment selection marker include assessing its prognostic value, evaluating treatment effects in patients with a restricted range of marker values, and testing for a statistical interaction between marker value and treatment. These methods are inadequate, because they give misleading measures of performance that do not answer key clinical questions about how the marker might help patients choose treatment, how treatment decisions should be made on the basis of a continuous marker measurement, what effect using the marker to select treatment would have on the population, or what proportion of patients would have treatment changes on the basis of marker measurement. Marker-by-treatment predictiveness curves are proposed as a more useful aid to answering these clinically relevant questions, because they illustrate treatment effects as a function of marker value, outcomes when using or not using the marker to select treatment, and the proportion of patients for whom treatment recommendations change after marker measurement. Randomized therapeutic clinical trials, in which entry criteria and treatment regimens are not restricted by the marker, are also proposed as the basis for constructing the curves and evaluating and comparing markers.

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.

Genetic variation in radiation and platinum pathways predicts severe acute radiation toxicity in patients with esophageal adenocarcinoma treated with cisplatin-based preoperative radiochemotherapy: results from the Eastern Cooperative Oncology Group

PURPOSE

Germline genetic variations may partly explain the clinical observation that normal tissue tolerance to radiochemotherapy varies by individual. Our objective was to evaluate the association between single-nucleotide polymorphisms (SNPs) in radiation/platinum pathways and serious treatment-related toxicity in subjects with esophageal adenocarcinoma who received cisplatin-based preoperative radiochemotherapy.

METHODS

In a multicenter clinical trial (E1201), 81 eligible treatment-naïve subjects with resectable esophageal adenocarcinoma received cisplatin-based chemotherapy concurrent with radiotherapy, with planned subsequent surgical resection. Toxicity endpoints were defined as grade ≥3 radiation-related or myelosuppressive events probably or definitely related to therapy, occurring during or up to 6 weeks following the completion of radiochemotherapy. SNPs were analyzed in 60 subjects in pathways related to nucleotide/base excision- or double stranded break repair, or platinum influx, efflux, or detoxification.

RESULTS

Grade ≥3 radiation-related toxicity (mostly dysphagia) and myelosuppression occurred in 18 and 33% of subjects, respectively. The variant alleles of the XRCC2 5' flanking SNP (detected in 28% of subjects) and of GST-Pi Ile-105-Val (detected in 65% of subjects) were each associated with higher odds of serious radiation-related toxicity compared to the major allele homozygote (47% vs. 9%, and 31% vs. 0%, respectively; P = 0.005). No SNP was associated with myelosuppression.

CONCLUSIONS

This novel finding in a well-characterized cohort with robust endpoint data supports further investigation of XRCC2 and GST-Pi as potential predictors of radiation toxicity.

Moving from prognostic to predictive factors in chronic lymphocytic leukaemia (CLL)

Many prognostic factors have been identified in chronic lymphocytic leukaemia (CLL). Based on the assessment of B cell receptor (BCR) structure and function, a subdivision into subtypes is possible (e.g., immunoglobulin heavy chain variable gene segment (IGHV) unmutated and mutated, V3-21 usage) with distinct biological and clinical characteristics. Recurrent genomic aberrations (i.e., 11q and 17p deletion) and gene mutations (i.e., TP53 and ATM) help to define biological and clinical subgroups. In addition, serum markers (e.g., thymidine kinase (TK) and beta2-microglobulin (beta2-MG)), cellular markers (e.g., CD38 and ZAP70) and clinical staging have an impact on outcome in CLL. The biological characterisation of CLL has not only led to progress in outcome prediction but also has begun to be translated into novel treatment strategies. Nonetheless, most factors associated with prognosis have not been thoroughly interrogated for their predictive value in the light of different therapeutic approaches. With a growing number of agents acting on specific biological targets and being used in different clinical situations, the future is likely to bring the identification of predictive factors in CLL.

Targeting population entering phase III trials: a new stratified adaptive phase II design

The primary goal of phase II studies is to assess the efficacy of the new treatment in order to decide whether it has sufficient activity to warrant further evaluation in a phase III comparative trial. However, many adequately conducted phase II trials are negative leading to termination of drug development. Heterogeneity of the population is often considered to be a cause of treatment effect dilution. One approach to determine the sensitive subpopulation is to conduct several phase II trials, one in each specific subset of patients. This option might unethically increase the number of non-sensitive patients under evaluation. Adaptive two-stage designs have been recently proposed. London and Chang proposed a global one-sample test for response rates for stratified phase II clinical trials, whereas Jones and Holmgren proposed an adaptive design that allows preliminary determination of efficacy that may be restricted to a specific subpopulation defined by biomarker status. These two methods do not allow early termination for efficacy in one or several subgroups as they are extensions of the Simon design. The authors propose an alternative method to deal with stratification in phase II clinical trials and identification of the best target population. This method is based on the multiple-stage Fleming design allowing for early stopping rules for either efficacy or inefficacy. It also integrates a procedure testing whether treatment effects are similar or heterogeneous between the two groups. The operating characteristics of this method were compared with those of a standard Fleming design using exact binomial probabilities.

[Patient stratification: interactions between diagnosis and therapy]

There is a high interest in developing diagnostic tests, e.g. in terms of molecular markers. Usually diagnostic tests are judged by their accuracy. The benefit of diagnostic testing for the patient, though, can only derive from the received treatment after diagnosis. In this way, interaction between diagnostic testing and therapy is a prerequisite. In such cases diagnostic testing is predictive of therapy. To show its interaction, diagnostic tests should be embedded in randomised controlled trials.

Breaking through a plateau in renal cell carcinoma therapeutics: development and incorporation of biomarkers

With the Food and Drug Administration approval of 6 novel targeted agents since December 2005 and limited comparative trials to discern relative efficacy, the treatment of metastatic renal cell carcinoma (RCC) has become immensely complex. The research community must look to novel ways in which to identify appropriate candidates for selected targeted therapies; one potential strategy is the use of clinical and molecular biomarkers. A growing body of knowledge-related von Hippel Lindau-driven pathways in this disease has highlighted the potential role of hypoxia-inducible factor subtypes in distinguishing RCC patients clinically. Techniques applied in other malignancies, such as gene expression and proteomic profiling, may also ultimately allow for clinical stratification. An emerging understanding of immunologic phenomena that may affect cancer progression (i.e., tumor infiltration by CD68 lymphocytes, memory T-cells, etc.) has unveiled a number of other potential biomarkers of response. Several vascular endothelial growth factor receptor-directed therapies classically thought to function as antiangiogenics may also have complex effects upon the tumor microenvironment including the associated immune cell milieu. As such, immunologic parameters could potentially predict response to current therapies. Finally, clinical biomarkers, such as hypertension, may predict the efficacy of several currently available targeted agents, although implementation of such biomarkers remains challenging. Herein, the clinical relevance of putative RCC biomarkers is examined in detail.

Statistical design of personalized medicine interventions: the Clarification of Optimal Anticoagulation through Genetics (COAG) trial

Background

There is currently much interest in pharmacogenetics: determining variation in genes that regulate drug effects, with a particular emphasis on improving drug safety and efficacy. The ability to determine such variation motivates the application of personalized drug therapies that utilize a patient's genetic makeup to determine a safe and effective drug at the correct dose. To ascertain whether a genotype-guided drug therapy improves patient care, a personalized medicine intervention may be evaluated within the framework of a randomized controlled trial. The statistical design of this type of personalized medicine intervention requires special considerations: the distribution of relevant allelic variants in the study population; and whether the pharmacogenetic intervention is equally effective across subpopulations defined by allelic variants.

Methods

The statistical design of the Clarification of Optimal Anticoagulation through Genetics (COAG) trial serves as an illustrative example of a personalized medicine intervention that uses each subject's genotype information. The COAG trial is a multicenter, double blind, randomized clinical trial that will compare two approaches to initiation of warfarin therapy: genotype-guided dosing, the initiation of warfarin therapy based on algorithms using clinical information and genotypes for polymorphisms in CYP2C9 and VKORC1; and clinical-guided dosing, the initiation of warfarin therapy based on algorithms using only clinical information.

Results

We determine an absolute minimum detectable difference of 5.49% based on an assumed 60% population prevalence of zero or multiple genetic variants in either CYP2C9 or VKORC1 and an assumed 15% relative effectiveness of genotype-guided warfarin initiation for those with zero or multiple genetic variants. Thus we calculate a sample size of 1238 to achieve a power level of 80% for the primary outcome. We show that reasonable departures from these assumptions may decrease statistical power to 65%.

Conclusions

In a personalized medicine intervention, the minimum detectable difference used in sample size calculations is not a known quantity, but rather an unknown quantity that depends on the genetic makeup of the subjects enrolled. Given the possible sensitivity of sample size and power calculations to these key assumptions, we recommend that they be monitored during the conduct of a personalized medicine intervention.

Trial Registration

clinicaltrials.gov: NCT00839657

Expression of p16 in lymph node metastases of adjuvantly treated stage III colorectal cancer patients identifies poor prognostic subgroups: a retrospective analysis of biomarkers in matched primary tumor and lymph node metastases

BACKGROUND

The objective of identifying protein biomarkers for patients with stage III and IV colorectal cancer is to improve risk stratification and, thus, to identify patients in the postoperative setting who may benefit from more targeted treatment. The objective of the current study was to determine the prognostic value of 19 protein markers assessed in primary tumors and matched lymph node (LN) metastases from patients with stage III and IV colorectal cancer.

METHODS

Matched primary tumors and LN metastases from 82 patients with stage III and IV colorectal cancer were mounted onto a multiple-punch tissue microarray and were stained for 19 protein markers involved in tumor progression (β-catenin, E-cadherin, epidermal growth factor receptor, phosphorylated extracellular signal-regulated kinase [pERK], receptor for hyaluronic acid-mediated motility, phosphorylated protein kinase B, p21, p16, B-cell lymphoma 2, Ki67, apoptotic protease activating factor 1, mammalian sterile 20-like kinase 1, Raf kinase inhibitor protein, vascular endothelial growth factor, ephrin type-B receptor 2, matrix metalloproteinase 7, laminin5γ2, mucin 1 [MUC1], and caudal-related homeobox 2). The prognostic effects of biomarkers in both primary tumor and positive LNs were assessed.

RESULTS

MUC1, pERK and p16 in LN (P=.002, P=.014, and P=.002, respectively) had independent prognostic value. In patients with stage III disease who received adjuvant treatment, negative p16 expression was associated with highly unfavorable outcomes overall (hazard ratio [HR], 0.26; 95% confidence interval [CI], 0.1-0.6; P=.005) when the analysis was stratified by pathologic tumor classification (HR, 0.25; 95% CI, 0.1-0.7; P=.005), age (HR, 0.23; 95% CI, 0.1-0.6; P=.004), and LN ratio (HR, 0.26; 95% CI, 0.1-0.7; P=.007); and, in multivariate analysis, it was associated with performance status and the receipt of folic acid treatment (HR, 0.29; 95% CI, 0.09-0.89; P=.03).

CONCLUSIONS

The loss of p16 in LN metastases contributed to adverse outcomes in adjuvantly treated patients with stage III colorectal cancer independent of pathologic tumor classification, age, LN ratio, performance status, or folic acid treatment. The current results support the investigation of p16 as a prognostic and potential predictive biomarker for future randomized trials of patients with stage III colorectal cancer.

Akt inhibitors in clinical development for the treatment of cancer

IMPORTANCE OF THE FIELD

The evolution of targeted therapies is dependent upon identification of cellular moieties that can be pharmacologically modulated. As one such example, the serine-threonine kinase Akt was identified nearly two decades ago. Since then, its role in mediating multiple signaling cascades (ultimately leading to cell growth and proliferation) has since been identified. More recently, several agents have been developed that antagonize Akt--these agents are in various stages of clinical testing.

AREAS COVERED IN THIS REVIEW

Herein, we outline development of several promising Akt inhibitors, including perifosine, MK-2206, RX-0201, PBI-05204, GSK2141795 and others.

WHAT THE READER WILL GAIN

The reader will gain insight into the current pipeline of Akt inhibitors, and the degree to which these agents have been examined both clinically and preclinically.

TAKE HOME MESSAGE

With an emerging pipeline of agents targeting Akt, it will be critical to decipher which amongst them holds the greatest promise. Herein, we explore this drug pipeline and provide strategies for determining the future clinical application of these agents.

Designing a randomized clinical trial to evaluate personalized medicine: a new approach based on risk prediction

We define personalized medicine as the administration of treatment to only persons thought most likely to benefit, typically those at high risk for mortality or another detrimental outcome. To evaluate personalized medicine, we propose a new design for a randomized trial that makes efficient use of high-throughput data (such as gene expression microarrays) and clinical data (such as tumor stage) collected at baseline from all participants. Under this design for a randomized trial involving experimental and control arms with a survival outcome, investigators first estimate the risk of mortality in the control arm based on the high-throughput and clinical data. Then investigators use data from both randomization arms to estimate both the effect of treatment among all participants and among participants in the highest prespecified category of risk. This design requires only an 18.1% increase in sample size compared with a standard randomized trial. A trial based on this design that has a 90% power to detect a realistic increase in survival from 70% to 80% among all participants, would also have a 90% power to detect an increase in survival from 50% to 73% in the highest quintile of risk.

Clinical application of gene expression profiling in breast cancer

Breast cancer is a heterogeneous disease associated with variable clinical outcomes and response to therapy. Classic clinicopathologic factors associated with outcome include anatomic features associated with prognosis (eg, tumor size, number of positive regional lymph nodes) and biologic features associated with prognosis and/or predictive of response to specific therapies, usually by evaluating protein expression by immunohistochemistry (eg, estrogen and/or progesterone receptors) or amplification of a single gene (eg, HER2/neu). Gene expression profiling evaluating thousands of genes is now feasible, and has facilitated the development of multiparameter assays that may identify breast cancer subtypes associated with distinct clinical outcomes that were not previously recognized, or provide more accurate information about prognosis or response to specific therapies than may be provided by classic clinicopathologic features alone. Several multiparameter gene expression assays are commercially available, and additional assays are being developed that will facilitate more accurate therapeutic individualization.

Translational research in oncology: key bottlenecks and new paradigms

Translational research is about transforming progress in basic research into products that benefit patients. Here I discuss some of the key obstacles to effective translational research in oncology that have previously received limited attention. Basic research often does not go far enough for straightforward clinical translation, and long-term, high-risk endeavours to fill these key gaps have not been adequately addressed either by industry or by the culture of investigator-initiated research. These key gaps include the identification of causative oncogenic mutations and new approaches to regulating currently undruggable targets such as tumour suppressor genes. Even where an inhibitor of a key target has been identified, new approaches to clinical development are needed. The current approach of treating broad populations of patients based primarily on primary cancer site is not well suited to the development of molecularly targeted drugs. Although developing drugs with predictive diagnostics makes drug development more complex, it can improve the success rate of development, as well as provide benefit to patients and the economics of healthcare. I review here some prospective Phase III designs that have been developed for transition from the era of correlative science to one of reliable predictive and personalised oncology.

A Bayesian adaptive design with biomarkers for targeted therapies

BACKGROUND

Targeted therapies are becoming increasingly important for the treatment of various diseases. Biomarkers are a critical component of a targeted therapy as they can be used to identify patients who are more likely to benefit from a treatment. Targeted therapies, however, have created major challenges in the design, conduct, and analysis of clinical trials. In traditional clinical trials, treatment effects for various biomarkers are typically evaluated in an exploratory fashion and only limited information about the predictive values of biomarkers obtained.

PURPOSE

New study designs are required, which effectively evaluate both the diagnostic and the therapeutic implication of biomarkers.

METHODS

The Bayesian approach provides a useful framework for optimizing the clinical trial design by directly integrating information about biomarkers and clinical outcomes as they become available. We propose a Bayesian covariate-adjusted response-adaptive randomization design, which utilizes individual biomarker profiles and patient's clinical outcomes as they become available during the course of the trial, to assign the most efficacious treatment to individual patients. Predictive biomarker subgroups are determined adaptively using a partial least squares regression approach.

RESULTS

A series of simulation studies were conducted to examine the operating characteristics of the proposed study design. The simulation studies show that the proposed design efficiently identifies patients who benefit most from a targeted therapy and that there are substantial savings in the sample size requirements when compared to alternative designs.

LIMITATIONS

The design does not control for the type I error in the traditional sense and a positive result should be confirmed by conducting an independent phase III study focusing on the selected biomarker profile groups.

CONCLUSIONS

We conclude that the proposed design may serve a useful role in the early efficacy phase of targeted therapy development.

Prospective alpha allocation in the Clarification of Optimal Anticoagulation through Genetics (COAG) trial

BACKGROUND

The Clarification of Optimal Anticoagulation through Genetics (COAG) trial is a large, multicenter, double-blinded, randomized trial to determine whether use of a genotype-guided dosing algorithm (using clinical and genetic information) to initiate warfarin treatment will improve anticoagulation status when compared to a dosing algorithm using only clinical information.

PURPOSE

This article describes prospective alpha allocation and balanced alpha allocation for the design of the COAG trial.

METHODS

The trial involves two possibly heterogeneous populations, which can be distinguished by the difference in warfarin dose as predicted by the two algorithms. A statistical approach is detailed, which allows an overall comparison as well as a comparison of the primary endpoint in the subgroup for which sufficiently different doses are predicted by the two algorithms. Methods of allocating alpha for these analyses are given - a prospective alpha allocation and allocating alpha so that the two analyses have equal power, which we call a 'balanced alpha allocation.'

RESULTS

We show how to include an analysis of the primary endpoint in a subgroup as a co-primary analysis. Power can be improved by incorporating the correlation between the overall and subgroup analyses in a prospective alpha allocation approach. Balanced alpha allocation for the full cohort and subgroup tests to achieve the same desired power for both of the primary analyses is discussed in detail.

LIMITATIONS

In the COAG trial, it is impractical to stratify the randomization on subgroup membership because genetic information may not be available at the time of randomization. If imbalances in the treatment arms in the subgroup are found, they will need to be addressed.

CONCLUSIONS

The design of the COAG trial assures that the subgroup in which the largest treatment difference is expected is elevated to a co-primary analysis. Incorporating the correlation between the full cohort and the subgroup analyses provides an improvement in power for the subgroup comparison, and further improvement may be achieved via a balanced alpha allocation approach when the parameters involved in the sample size calculation are reasonably well estimated.

Clinical, laboratory and molecular factors predicting chemotherapy efficacy and toxicity in colorectal cancer

Colorectal cancer (CRC) treatment has evolved significantly over the last ten years with the use of active chemotherapeutic agents including fluoropyrimidines, oxaliplatin and irinotecan plus targeted monoclonal antibodies bevacizumab, cetuximab and panitumumab. The addition of newer chemotherapeutic agents and targeted therapies has improved patient outcomes at the cost of increased toxicity with not all patients benefiting from these treatments. It is necessary for clinicians to more accurately predict clinical outcomes particularly in the predominantly elderly CRC patient population. This review aims to summarise existing data regarding the use of clinical and laboratory variables plus molecular markers in predicting response, survival and toxicity to chemotherapy agents and targeted monoclonal antibodies currently used in the treatment of CRC.