Cancer Center and licensed to Castle Biosciences for clinical and research purposes

  - Clinical Development
     - Discovery Background
     - Additional Development and Validation
     - Sample Report
     - Publication List
  - Reimbursement

Discovery Background

DecisionDx-GBM is a proprietary multi-gene expression test that analyses the genetic footprint of your patient's GBM tumor and, using an algorithm, compares the output to the underlying clinical database.

DecisionDx-GBM was discovered at The University of Texas M. D. Anderson Cancer Center and licensed to Castle Biosciences for clinical and research purposes. Castle Biosciences has performed and completed CLIA development and validation through its licensed laboratory.

As shown in the table below a number of product development items were proactively identified and addressed during the development phase of the assay.

Development Items	Solution
Generation of false positives due multiple comparisons of a single dataset	Use multi-institutional sample sets in discovery effort to identify robust gene set.
Specimen platform that is widely available	While there are benefits to using fresh frozen tumor samples for RNA analysis, the individual patient use of this test would be limited if it were dependent upon fresh frozen tissue. Therefore, an early aim of the inventors was to transfer the platform to using formalin-fixed paraffin embedded (FFPE) tissue. This transfer was successful.
Improve efficiency	While the discovery efforts identified a robust set of 38 genes that were common among all four of the multi-institutional sample set, efforts were planned to reduce the gene set to the most efficient set possible. We were able to reduce the set to 9 genes of interest (plus 3 controls).

One of the issues in identifying biomarkers is the potential for generating false positive biomarkers as a result of running multiple comparisons from a single dataset. One of the methods to control for this issue is to use independent sample sets during the discovery process. Sample sets from Massachusetts General Hospital (MGH), ULCA, UCSF and M. D. Anderson Cancer Center.

The discovery effort focused on identifying the most robust, top 200, survival genes in each of these sample sets. As shown in the Venn diagram below, 38 genes were robustly associated with all four sample sets. Additional analysis using scrambled survival data showed a false discovery rate estimated at 0.3%. While SAM, Rank Product, an Cox model were all conducted, the largest overlap of these consensus genes was identified through the fold-change method. The fold-change method was used for all subsequent analysis and in the clinical test.

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Additional Development and Validation

The 38 consensus genes identified in the multi-institutional analysis (Study 1) were validated in a subsequent dataset (Study 2) of FFPE tissue. Study 2 also identified an improved gene set of 9. The 9 gene set was subsequently validated in Study 3.

Validation studies for DecisionDx-GBM were carried out in two phases. The Kaplan-Meier curve below shows results from all 169 patients, divided into 2 groups based upon results from recursive partitioning analysis.

Through multi-variate analysis, DecisionDx-GBM was found to be an independent predictor of progression free survival (HR = 2.7, p=0.0003; Cox)and overall survival (HR = 2.7, p=0.0003; Cox) relative to the clinical factors of age and performance score (KPS).

MGMT methylation was performed in Study 2. Through multi-variate analysis, DecisionDx-GBM was found to be an independent predictor of survival relative to MGMT methylation status. MGMT methylation status was found not to be an independent predictor of survival in this same study.

Log Rank Analysis
	Overall Survival
DecisionDx-GBM	P=0.0096
MGMT methylation status	P=0.0556

Cox Multivariate Analysis
	Hazard Ratio	p-value
DecisionDx-GBM	3.3	0.0055
MGMT methylation status	1.9	0.0602

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Sample Report

Click the icon below to view a sample report from a patient whose DecisionDx-GBM raw score placed them in the 1st quintile of our clinical database.

Sample Report

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Publication List

Colman, et al. 2006. Meta-analysis of gene expression profiling data from glioblastoma tumor samples identifies a robust Multigene classifier predictive of survival. Proc Amer Assoc Cancer Res, Volume 47, Abstract # 5688.

Colman, et al. 2007. A robust Multigene classifier predictive of survival in patients with newly diagnosed glioblastoma. Proc Amer Assoc Cancer Res. Vol , Abstract #2700.

Aldape, et al. 2006. Meta-analysis of gene expression profiling data from glioblastoma tumor samples identifies a robust multigene classifier predictive of survival. Journal of the Society for Neuro-Oncology. Abstract GE-01.

Colman, et al. 2009. A multigene predictor of outcome in glioblastoma. Neuro-Oncology. Advanced Access published, Oct 20, 2009.

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Reimbursement

We understand that paying for elements of healthcare can be confusing and overwhelming. As a company, we are sensitive to that fact and are prepared to assist patients through the process of securing payment for services provided by Castle. We will coordinate the submission of insurance claims for Medicare and commercial insurance companies. We will work with the patient's authorized healthcare provider regarding benefits investigation should one be requested, any prior authorization required, and appeals processing. In cases when, after all appeals are exhausted, the claim is not fully paid then the patient will ultimately be responsible for any balance remaining.

The Castle Patient Assistance Program is available to explore options for patients with balances remaining. This program also has options for uninsured patients.

In some cases, institutions may ask us to invoice them directly, also known as client bill.

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