Invited Sessions Details

Development and Evaluation of Biomarkers for Predicting Treatment Effects in Clinical Trials: Methodology and Application

Presenter: Parvin Tajik

Session Synopsis:

Treatment selection for patients with ovarian cancer

Despite the growing interest in developing markers for predicting treatment response and to optimize treatment decisions, there has only been a slow development in appropriate methodology to evaluate these markers and a slower progress in application of these methods in clinical research. Most researchers still either test for marker-treatment statistical interaction or assess the prognostic value of the markers, which are not optimal and sometimes even misleading. In this talk we will show applications of some recently proposed performance measures for evaluating proposed makers in ovarian cancer. Ovarian cancer is one of the most lethal malignancies in women and despite improvements in treatment strategies such as cytoreductive surgery, combination chemotherapy and targeted molecular therapy, survival rates have only increased modestly over the past decades. Since the disease exhibits significant heterogeneity at clinical, histo-pathological and molecular levels, there is hope that a higher survival of patients can be achieved if markers can accurately predict the effects of the treatments and therefore guide the selection of the optimal treatment for patient. Disease stage, histologic type, tumor grade, debulking status, serum CA-125 levels in combination with the CT imaging are the established prognosticators in ovarian cancer and there are reports on molecular tissue biomarkers such as somatic mutations in KRAS, BRAF, EGFR and PTEN. We use data from two randomized trials of European Organization for Research and Treatment of Cancer (EORTC) 55971 and 55041. We illustrate the value of the above mentioned markers for predicting treatment effects using both overall and personalized performance measures. We finish with a discussion of the advantages and limitations of each measure from clinical perspective, as well as directions for future work in this area.

Development and Evaluation of Biomarkers for Predicting Treatment Effects in Clinical Trials: Methodology and Application

Presenter: Noah Simon

Session Synopsis:

Adaptive Enrichment Trials for Biomarker-guided Treatments

The biomedical field has recently focused on developing targeted therapies, designed to be effective in only some subset of the population with a given disease. However, for many new treatments, characterizing this subset has been a challenge. Often, at the start of large-scale trials the subset is only rudimentarily understood. This leads practitioners to either 1) run an all-comers trial without use of the biomarker or 2) use a poorly characterized biomarker that may miss parts of the true target population and potentially incorrectly indicate a drug from a successful trial. In this talk we will discuss a class of adaptive enrichment designs: clinical trial designs that allow the simultaneous construction and use of a biomarker, during an ongoing trial, to adaptively enrich the enrolled population. For poorly characterized biomarkers, these trials can significantly improve power while still controlling type one error. However there are additional challenges in this framework: How do we adapt our enrollment criteria in an �optimal� way? (what are we trying to optimize for?) How do we run a formal statistical test after updating our enrollment criteria? How do we estimate an unbiased treatment effect-size in our �selected population�? (combatting a potential selection bias) In this talk we will give an overview of a class of clinical trial designs and tools that address these questions.The biomedical field has recently focused on developing targeted therapies, designed to be effective in only some subset of the population with a given disease. However, for many new treatments, characterizing this subset has been a challenge. Often, at the start of large-scale trials the subset is only rudimentarily understood. This leads practitioners to either 1) run an all-comers trial without use of the biomarker or 2) use a poorly characterized biomarker that may miss parts of the true target population and potentially incorrectly indicate a drug from a successful trial. In this talk we will discuss a class of adaptive enrichment designs: clinical trial designs that allow the simultaneous construction and use of a biomarker, during an ongoing trial, to adaptively enrich the enrolled population. For poorly characterized biomarkers, these trials can significantly improve power while still controlling type one error. However there are additional challenges in this framework: How do we adapt our enrollment criteria in an �optimal� way? (what are we trying to optimize for?) How do we run a formal statistical test after updating our enrollment criteria? How do we estimate an unbiased treatment effect-size in our �selected population�? (combatting a potential selection bias) In this talk we will give an overview of a class of clinical trial designs and tools that address these questions.The biomedical field has recently focused on developing targeted therapies, designed to be effective in only some subset of the population with a given disease. However, for many new treatments, characterizing this subset has been a challenge. Often, at the start of large-scale trials the subset is only rudimentarily understood. This leads practitioners to either 1) run an all-comers trial without use of the biomarker or 2) use a poorly characterized biomarker that may miss parts of the true target population and potentially incorrectly indicate a drug from a successful trial. In this talk we will discuss a class of adaptive enrichment designs: clinical trial designs that allow the simultaneous construction and use of a biomarker, during an ongoing trial, to adaptively enrich the enrolled population. For poorly characterized biomarkers, these trials can significantly improve power while still controlling type one error. However there are additional challenges in this framework: How do we adapt our enrollment criteria in an �optimal� way? (what are we trying to optimize for?) How do we run a formal statistical test after updating our enrollment criteria? How do we estimate an unbiased treatment effect-size in our �selected population�? (combatting a potential selection bias) In this talk we will give an overview of a class of clinical trial designs and tools that address these questions.The biomedical field has recently focused on developing targeted therapies, designed to be effective in only some subset of the population with a given disease. However, for many new treatments, characterizing this subset has been a challenge. Often, at the start of large-scale trials the subset is only rudimentarily understood. This leads practitioners to either 1) run an all-comers trial without use of the biomarker or 2) use a poorly characterized biomarker that may miss parts of the true target population and potentially incorrectly indicate a drug from a successful trial. In this talk we will discuss a class of adaptive enrichment designs: clinical trial designs that allow the simultaneous construction and use of a biomarker, during an ongoing trial, to adaptively enrich the enrolled population. For poorly characterized biomarkers, these trials can significantly improve power while still controlling type one error. However there are additional challenges in this framework: How do we adapt our enrollment criteria in an �optimal� way? (what are we trying to optimize for?) How do we run a formal statistical test after updating our enrollment criteria? How do we estimate an unbiased treatment effect-size in our �selected population�? (combatting a potential selection bias) In this talk we will give an overview of a class of clinical trial designs and tools that address these questions.

Development and Evaluation of Biomarkers for Predicting Treatment Effects in Clinical Trials: Methodology and Application

Presenter: Eric Laber

Session Synopsis: