1. Data Analysis Summary

2. Elephant in the room

3. General Comments General understanding that informatics is integral in medical sequencing and other –omics in clinical settings About 80% of attendees were actively involved in data analysis Clinical practitioners also present Talking about data analysis is difficult – We do not yet have language to do so – Complicated – Not clear what details are important

4. Overview Analytical Validity Enhance Clinical Utility Data Sharing Messages to NCI

5. Analytical Validity Definition of “mutation”, both at the level of variant calling and with “established” calls, remains unclear Reproducible analytical methods in both research and clinical practice needed – Versioning of raw data and processed data – Versioning of data analysis pipelines – Versioning of auxiliary data (gene models, sequences, etc.)

6. Analytical Validity Understanding that algorithms that use all the information (tumor/normal or multiple tumor samples) yields higher sensitivity and specificity – Data archiving and sharing becomes important – Archiving blocks is not enough—best to archive data as well Need to provide confidence associated with variants since no test is 100% accurate

7. Analytical Validity “Reference” genome – A computational reference is important to allow communication of findings – Lack of adequate ground-truth datasets precludes rigorous evaluation of analysis, particularly in quantifying false-negative rate Tumor heterogeneity, tissue heterogeneity, and even stochastic sampling at the sample level remain challenges in establishing analytical validity

8. Enhance Clinical Utilty Lacking large (10s of thousands of patients), well- annotated databases of normal or disease patients Definition of clinically actionable remains unclear Reference database of clinically actionable variants – Does not exist – Will be challenging to update and maintain – Incorporating clinical context is difficult but probably necessary if one is to truly achieve precision medicine

9. Enhance Clinical Utility Establish methods of reporting that empower the clinician – Enough detail to be helpful – Not so much detail as to be unintelligible – Integrate with online databases and knowledge

10. Data Sharing Need to establish standards for data sharing in both research and clinical venues (think Myriad and BRCA1 testing) – Protocols, both computational and laboratory – Controlled vocabularies – Clinical data – The data themselves Consider incentivizing data sharing – Pay-to-play sharing – NCI mandate

11. Data Sharing What constitutes de-identified data? – Need to respect rights of patients, including protecting AND sharing data Need some way to feed clinical information back to into the informatics pipeline – Clinicians need actionable information with as much interpretation with regard to literature and knowledgebase as possible

12. Messages to NCI Critical need to establish ground truth datasets and biologics Fix TCGA! NCI should collect and maintain knowledgebase of “clinically actionable” information (variants, genes, pathways) – Start by collecting and updating lists from large medical centers Enhance PDQ database to include computable information on molecular targets under study

13. Messages to NCI More input is needed when NCI is planning bioinformatics, computational biology, and biomedical informatics – Granting mechanism? – Less top-down approach to informatic Establish and ENFORCE rational data sharing mechanisms for NCI-sponsored clinical trials – SRA is not the answer….

14. phenotype Gene Copy Number Sequence Variation Chromatin Structure and Function Gene Expression Transcriptional Regulation DNA Methylation Patient and Population Characteristics

15. Questions