Clinical trial NCT01124461: BIRN (Biomedical Informatics Research Network) Resources Facilitate the Personalization of Malignant Brain Tumor
Official title: BIRN (Biomedical Informatics Research Network)Resources Facilitate the Personalization of Malignant Brain Tumor
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: GLIOBLASTOMA MULTIFORME
Brain Neoplasm
Keywords: Malignant Brain Tumors
Cancer of the Brain
Lead sponsor: Washington University School of Medicine
Sponsor class: OTHER
Collaborators: name: National Institute of Neurological Disorders and Stroke (NINDS)
First posted: 2010-05-17
Enrollment: 112
Sex: ALL
Minimum age (years): 18
Maximum age (years): 70
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Neurology clinic

Brief summary: The goal of this study is to create a comprehensive database of Magnetic Resonance Imaging (MRI) and of pathology for patients with brain tumors. Both standard, advanced, and research MRI components may be included, these will be analyzed in comparison with pathology results if/when a biopsy is obtained, and also used to predict/evaluate responses to therapy. This study will create a database of de-identified MRI images which include these techniques so that brain tumors can be studied over time (longitudinally) in an organized manner.

Detailed description: Glioblastoma (GBM) is the most common primary malignant neoplasm of the adult brain. Even after multimodal therapy, treatment outcomes remain poor, with a median survival of approximately one year. A central challenge facing investigators in the modern era is how to resolve the heterogeneity inherent in GBM pathology using technology and how to identify individual genetic or molecular markers that indicate how treatment can be individualized to improve outcomes with an emphasis on using this heterogeneity to improve patient care. With advances in imaging and the potential for genetic sequence analysis, increasingly clinicians and researchers have focused on specific clinical, imaging, and genetic biomarkers to allow the personalization of brain tumor treatment in an attempt to improve the limitations we have faced in extending patient survival from this devastating disease. Specific methodologies have been developed to allow genetic microarray analysis of patient's tumor tissue, and this type of research is ongoing at one of our participating institutions, Swedish Medical Center. In addition, centers such as Washington University School of Medicine in St. Louis, Missouri have extensive experience pursuing advanced imaging biomarkers and their applications to clinical neuro-oncology research.

Of importance, however, although clinicians and researchers have come to recognize that in-vivo imaging technologies may have as much if not more relevance than genetic biomarkers in the personalization of brain tumor treatment, clinical trials attempting to validate these biomarkers and correlate them with particular outcomes have been limited by a lack of technology infrastructure that would allow multi-site image acquisition, processing, data analysis, subsequent correlation with clinical and genetic data, and ultimately sharing of anonymized data with other researchers from a central archiving site. BIRN infrastructure will integrate neuroimaging, genetic microarray, and clinical data with a focus on integrating imaging biomarkers into prospective clinical research in patients with malignant brain tumors.

In this project, a consortium of neuro-oncology research centers will be federated to obtain a unified set of clinical, genetic, and imaging data. In the initial phase, 100 patients with malignant brain tumors at two participating sites will be studied. Our ultimate goal will be to use the developed protocols and informatics infrastructure to expand the consortium to include a large number neuro-oncology clinical sites suitable for executing large scale clinical trials that will facilitate the generation of data to identify which imaging biomarkers are relevant for the personalization of brain tumor treatment and ultimately improvement of outcomes for patients with this devastating disease.

Eligibility criteria: Inclusion Criteria:

* Patients will be enrolled in this clinical trial pre-operatively anticipating a surgical resection with anticipation that the patient carries a likely diagnosis of malignant glioma.
* Patients will only be enrolled if it is anticipated that the resection will give significant tissue for subsequent genetic analysis (1 cm of tumor tissue)
* If patients at surgery are found to carry an alternative tissue diagnosis, the patient's preoperative imaging, clinical, and pathological information will be uploaded into the database, but the patient will not be counted as one of the participants
* Ability to undergo serial MR studies
* Enrollment KPS \> 70
* Anticipation that surgery will allow subtotal resection or gross total resection, facilitating removal of tissue specimens for genomic analysis.

Exclusion Criteria:

* Inability to participate in serial MR studies
* KPS \< 70
* \> 70 years of age

Primary outcomes:
Secondary outcomes:
Other outcomes:
Locations:
city: Saint Louis
country: United States
facility: Washington University School of Medicine
state: Missouri
Study officials:
affiliation: Washington University School of Medicine
name: Daniel Marcus, PhD
role: PRINCIPAL_INVESTIGATOR
References:
citation: Prior FW, Fouke SJ, Benzinger T, Boyd A, Chicoine M, Cholleti S, Kelsey M, Keogh B, Kim L, Milchenko M, Politte DG, Tyree S, Weinberger K, Marcus D. Predicting a multi-parametric probability map of active tumor extent using random forests. Annu Int Conf IEEE Eng Med Biol Soc. 2013;2013:6478-81. doi: 10.1109/EMBC.2013.6611038.