Improving the Quality of Cancer Clinical Trials Workshop Summary (2008) / Chapter Skim
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Screening for Predictive Markers
Screening for Predictive Markers
Pages 49-66
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From page 49... ...
Drs. Pierre Massion of Vanderbilt Ingram Cancer Center, James Heath of California Institute of Technology, Dan Sullivan of Duke University, and Daniel Von Hoff of Translational Genomics Research Institute addressed these issues in the presentations, and provided answers to specific questions posed by the National Cancer Policy Forum at a panel discussion that followed the presentations.
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From page 50... ...
These include the ER and HER-2/neu biomarker tumor tests that predict response to various breast cancer treatments, gene signature tumor tests based on the activation patterns of 21 or 70 genes that predict breast cancer recurrence or survival (Paik et al., 2004) , and a tumor genetic signature test based on 100 genes that can distinguish between two types of similarly appearing lymphomas, and predict survival following treatment (Dave et al., 2006)
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From page 51... ...
Under the auspices of the NCI and the Specialized Programs of Research Excellence (SPORE) program, he and other researchers have created a group called the Lung Cancer Biomarkers Group, which aims to provide several academic institutions with access to four different sets of patient sample materials held at an NCI repository for the purpose of testing the accuracy of lung cancer biomarker tests.
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From page 52... ...
Another study design does not address the quality or value of the biomarker itself, but rather compares outcomes for two different interventions in those who test positive for the biomarker and as well as those who test negative for Marker Assessment Marker + Marker – Screen or No Screen Intervene Compare to Controls FIGURE 14 Clinical utility of predictive markers, study design 1: A single-arm validation study using historical controls for comparison. In this study, all patients receive the biomarker test and outcomes of patients who test positive for the biomarker are compared with those of historical controls.
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From page 53... ...
. fig 15 Randomization Screen Intervention A Marker + Marker Assessment Screen Intervention B Randomization Screen Intervention A Marker – Screen Intervention B FIGURE 16 Clinical utility of predictive markers, study design 3: Randomization of treatment irrespective of biomarker test results.
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From page 54... ...
Biomarker tests should also require very small amounts of tissue or blood, such as a finger prick of blood, yet be highly sensitive and quantitative because many of the compounds of interest are present in exquisitely minute amounts in patient samples. To improve the sensitivity and quantitativeness of such tests, Dr.
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From page 55... ...
Sullivan gave examples of how bioimaging can be used to predict clinically relevant variables in oncology; the advantages and disadvantages of using imaging biomarkers; and the technical and regulatory challenges of making those biomarkers clinically useful. As previous speakers noted, bioimaging can predict where a cancer drug will concentrate in the body, and various physiologic states such as a lack of oxygen (hypoxia)
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From page 56... ...
Another advantage of molecular imaging over some genetic or proteomic biomarker tests is that it can be less invasive because it does not require tumor samples. But several technical challenges are involved in developing imaging biomarkers, particularly if they use radiolabels and small molecules.
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From page 57... ...
However, although this may enable easier labeling synthesis, the labeled ligand may not reveal drug localization as accurately as the labeled drug and requires more validation. The time course for development of such labeled ligands can be as long or longer than that for labeled drugs, so that also may be out of synch with corresponding drug development.
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From page 58... ...
She gave an example of a small bioimaging study of a VEGF receptor inhibitor that showed that colorectal tumor vascularity and permeability decreased rapidly following treatment with the inhibitor and seemed to correlate with clinical benefit (Morgan et al., 2003, and Steward et al., 2004)
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From page 59... ...
"The FDA could develop a path which would allow for qualification of these individual biomarkers for the specific biochemical or pathway purpose for which they are intended," he said. Clinical Translation Given the complexity of the molecular mechanisms that underlie specific cancers and the challenges involved in developing and validating biomarker tests predictive of those mechanisms, translating the research findings on predictive biomarkers into tests with clinical usefulness can appear to be an especially difficult hurdle to overcome.
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From page 60... ...
Ewing sarcoma Phosphoinositide 3- SF1126 kinase (enzyme) Synovial cell sarcoma Gene translocation, Iressa/Tarceva Growth factor receptor Chondrosarcoma Tumor necrosis factor- TRAIL interactive agent related apoptosis inducing ligand (TRAIL)
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From page 61... ...
Chronic Neutrophilic Oncogene�������� -- ������� fusion Gleevec Leukemia protein (Brc-abl) Hypereosinophilic Growth factor receptor Gleevec syndrome mutations Medulloblastoma Growth factor receptor Gleevec, hedgehog mutations Gastrointestinal Stromal Growth factor receptor Gleevec, sunitinib Tumor mutation Prostate cancer Oncogene -- fusion HDAC inhibitor protein reversing the phenotype Castleman disease Increased interleukin-6 CNTO 328 (growth factor)
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From page 62... ...
Patients who have their tumors analyzed at TBAC have the opportunity to participate in Phase I or II clinical trials enriched with patients whose tumors have specific molecular abnormalities. Such clinical trials are models for the approval of a new agent aimed at a specific molecular target in a patient's tumor rather than designated for a particular histologic type of cancer, Dr.
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From page 63... ...
If the patient experiences a positive response, the therapy is continued. ACRONYMS: FGFR (fibroblast growth factor receptor)
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From page 64... ...
Von Hoff estimates that with this targeted approach to treatment, response rates range from 26 to 30 percent, and the response rates are even higher for patients with rare tumors. Meanwhile the average response rate for patients in Phase I clinical trials is about 4 percent.
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From page 65... ...
"If you address lung cancer as a whole, for example, you are limiting yourself and then you are actually going to face a lot of difficulty in applying biomarkers to specific subgroups. We should consult with our clinicians and epidemiologists to think about biomarker discovery in the specific clinical context and then rapidly test the biomarker in that context, and our preclinical models should mimic that clinical context." Later in the discussion, Dr.
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From page 66... ...
Sullivan if the use of predictive markers would increase the number of patients willing to participate in cancer clinical trials. Noting he had no data to back up his opinion, Dr.
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