December 2006 MAGE and the Biospecimen Research Database Experiment Design and other issues Ian Fore, D.Phil U.S. National Cancer Institute - Center for Bioinformatics Incorporating material from NCI Office of Biorepositories and Biospecimen Research January 24, 2008

Why? View from the outside Discussion of experimental factor Want not to lose a key feature Identify some issues Expect many are already in hand State the use case So we don’t lose the feature Describe how we are using experimental factor in the Biospecimen Research Database Inspired by MAGE model! Vision for how it should interact with MAGE

Pre- and Post- Acquisition Variables Impact Clinical and Research Outcomes Effects on Clinical Outcomes Potential for incorrect diagnosis Morphological/immunostaining artifact Skewed clinical chemistry results Potential for incorrect treatment Therapy linked to a diagnostic test on a biospecimen (e.g., HER2 in breast cancer) Effects on Research Outcomes Irreproducible results Variations in gene expression data Variations in post-translational modification data Misinterpretation of artifacts as biomarkers

Variables of Biospecimen Acquisition Post-acquisition variables:  Time at room temperature  Temperature of room  Type of fixative  Time in fixative  Rate of freezing  Size of aliquots  Type of collection container  Biomolecule extraction method  Storage temperature  Storage duration  Storage in vacuum Pre-acquisition variables:  Antibiotics  Other drugs  Type of anesthesia  Duration of anesthesia  Arterial clamp time  Blood pressure variations  Intra-op blood loss  Intra-op blood administration  Intra-op fluid administration  Pre-existing medical conditions  Patient gender

Use case 1 Actor - An investigator who does not work in a microarray lab and who does not understand the details of how microarray experiments are performed Description: The investigator wishes to understand what happens to gene expression under particular experimental conditions. They are happy to make the assumption that the experiments were done correctly and that appropriate QA and/or peer review of that took place. They want to work with the “high level” output from microarray experiments - experimental factors vs gene expression.

Experiment design - example Drug Control WY27127 WY26382 Dose 10, 30, 100, 300 mg/kg Time -10, -5, 0, 5, 10, 20, 30, 60, 120 mins Rat 4 rats per drug treatment

Dose in protocol text mg/kg/day <Protocol text="Rat treated q.d. by oral gavage with 200 mg/kg/day clofibrate (formulated in 0.5 % methylcellulose (w/v) plus 0.1 % polysorbate 80 (v/v) in distilled water). Animal sacrificed after five days, at which time a portion of liver was removed and flash frozen in liquid N2, then stored at -80 C. All experimental procedures were approved by the Pharmacia Institutional Animal Care and Use Committee and were performed in compliance with laws regarding humane treatment of laboratory animals." identifier="P-MEXP-357" name="SAMPLETREATPRTCL357">

Dose in protocol text mg/kg/day <Protocol text="Rat treated q.d. by oral gavage with 400 mg/kg/day clofibrate (formulated in 0.5 % methylcellulose (w/v) plus 0.1 % polysorbate 80 (v/v) in distilled water). Animal sacrificed after five days, at which time a portion of liver was removed and flash frozen in liquid N2, then stored at -80 C. All experimental procedures were approved by the Pharmacia Institutional Animal Care and Use Committee and were performed in compliance with laws regarding humane treatment of laboratory animals." identifier="P-MEXP-357" name="SAMPLETREATPRTCL357">

Use case 2 Actor - A scientist who wants to analyze microarray data who only has moderate understanding of statistical analysis techniques. (different person than the previous use case) Description - The scientist uses a statistical analysis tool which uses a wizard like approach to help them understand how the data might be analyzed. The microarray experiment data contains sufficient description for the statistical tool to extract the design of the experiment in a way that it can suggest appropriate ways to analyze the data. Implementation notes: Need the factors And their nature in the design - i.e. whether “intended” or not

Experiment design - example Drug - “intended” Control WY27127 WY26382 Dose - “intended” 10, 30, 100, 300 mg/kg Time - “intended” -10, -5, 0, 5, 10, 20, 30, 60, 120 mins Rat 4 rats per drug treatment

Experiment design - example Drug - “fixed effect” Control WY27127 WY26382 Dose - “fixed effect” 10, 30, 100, 300 mg/kg Time - “fixed effect” -10, -5, 0, 5, 10, 20, 30, 60, 120 mins Rat- “random effect” 4 rats per drug treatment

Use case 3 Actor - A database or system that wishes to index and publish microarray data This is not the same thing as a microarray data repository - it is a more topic based database or datamart. Description: The database wants to be able to automatically extract the experiment design from the experimental data file Example: Biospecimen Research Database

Use case 4 Actor - A scientist investigating cancer or another disease Description: The scientist understands that certain pre-analytical factors influence gene expression and that specimens must be collected in a way to remove those as variables from the experiment. They know that sometimes they do not follow the protocol exactly and wish to annotate their experiments with those variations from protocol. They visit a site such as the BRD which provides them with structured tissue processing protocols. They are able to download electronic of these protocols in MAGE or MAGE like format. The structured information therein can be used by programs they use to do their experiments and complete the “protocol execution” data specific to their experiment when they run them.

Use case 5 Actor - A scientist investigating cancer or another disease Description: The scientist knows that they were not able to control all factors in the experiment - such as those performed in by the surgeon or anesthetist during surgery. However, the BRD provides information about which genes are affected by these uncontrolled factors. They download gene lists from the BRD and remove these from consideration as factors relevant to cancer.

MAGE-ML overhead