CTF 2017 Cutaneous Neurofibroma Resource Sage Bionetworks

1. The molecular landscape of cutaneous neurofibroma Robert Allaway, PhD CTF Conference 2017 @allawayr

2. The molecular landscape of cutaneous neurofibroma Robert Allaway, PhD CTF Conference 2017 @allawayr accelerating biomedical research through open practices and collaborative research efforts

3. CME Disclosure • I have no financial relationships to disclose • I will not be discussing off-label or investigational uses of drugs or devices

4. Cutaneous/dermal neurofibromas • Grow from nerves in the skin • Late childhood throughout adulthood • Can range in both size and amount (few to 1000s) • Do not progress to malignancy • Cause lifelong struggle for NF1 patients • Can itch or be painful, cause superficial infections • Cosmetic, social concerns • Significant patient burden

5. Ongoing questions in cNF • Key driving components? • How do cutaneous NF differ from plexiform NF (molecular, microenvironment, endocrine, other)? • Diversity among patients? • Ideal therapeutic approaches?

6. Ongoing questions in cNF • Developing new treatments requires knowing why and how NFs grow • How best to facilitate & accelerate the pace of this research?

7. Hurdles in cNF research •What are some hurdles for the neurofibroma research community? • Few open datasets with comprehensively characterized biological samples

8. Hurdles in cNF research •What are some hurdles for the neurofibroma research community? • Few open datasets with comprehensively characterized biological samples • Identifying critical molecular features

9. Hurdles in cNF research •What are some hurdles for the neurofibroma research community? • Few open datasets with comprehensively characterized biological samples • Identifying critical molecular features • Patients are very diverse

10. CTF Biobank & Data Resource • CTF Biobank: • Neurofibroma and blood samples donated by patients

11. CTF Biobank & Data Resource •Data Resource: •Clinical and ‘omic data from biobank samples •Analysis •Share data and our analysis with NF researchers • CTF Biobank: • Neurofibroma and blood samples donated by patients

12. CTF Biobank & Data Resource

13. neurofibroma sample NF patient

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21. neurofibroma sample neurofibroma sample neurofibroma sample NF patient data analysis data analysis NF Research Community

23. www.synapse.org/cutaneousNF

24. Demographics Gosline et al, Scientific Data, 2017 11 patients 52 cNFs profiled

25. Data Type Number of Samples WGS 42 Patient matched PBMC for germline variants RNASeq 33 SNP Array 40 Patient matched PBMC for germline variants Proteomics 41 Currently being reprocessed Publically available data

26. WGS mutation profiling RNASeq WGS + RNASeq pathway analysis immune estimation integrative analysis

30. WGS mutation profiling RNASeq WGS + RNASeq pathway analysis immune estimation integrative analysis What genetic variants predominate across patients and tumors?

31. Broad array of germline and somatic NF1 mutations Germline

32. Broad array of germline and somatic NF1 mutations Germline Somatic

33. Landscape of germline mutations

34. Landscape of somatic mutations in cNF

35. WGS mutation profiling RNASeq WGS + RNASeq immune estimation pathway analysis integrative analysis Can we detect RNA-based signatures of immune components?

36. Immune population estimation • CIBERSORT (Newman et al 2015) • Estimation of immune cell fractions in tumor samples • Deconvolution method for RNA expression data • Identification of immune cell subtypes (LM22 signature set)

37. cNF immunologic microenvironment

38. WGS mutation profiling RNASeq WGS + RNASeq immune estimation pathway analysis integrative analysis Can we detect signaling pathway signatures?

39. Pathway analysis • ssGSEA: test for pathway enrichment across samples • Candidate-finding approach to discovering pathways for further investigation Barbie et al 2009, Hänzelmann et al 2013

40. enriched pathway signatures in cNF

41. WGS mutation profiling RNASeq WGS + RNASeq pathway analysis immune estimation integrative analysis What signatures correlate with mutations found in cNF?

42. Hotnet 2 42

43. When filtering for high-impact mutations, only 1 somatic network

44. When filtering for high-impact mutations, only 1 somatic network Highly mutated nodes

45. CDC27 and CREBBP have a broad mutation spectrum

46. Immune signatures correlate with CREBBP/CDC27 mutation Interferon 𝛄 Response signature Complement signature

47. Immune signatures correlate with CREBBP/CDC27 mutation Interferon 𝛄 Response signature Complement signature Can we leverage information like this to identify therapeutic approaches?

49. Conclusions Biobank and data resource are a resource for scientists studying cNF and other NF1-related tumors •Data sharing: • Eliminates hurdles for researchers • Enables stronger conclusions • Faciliates generation of new hypotheses • Allows the whole NF research community to participate, explore and ask new and different questions that may not have been tested

50. Conclusions Biobank and data resource are a resource for scientists studying cNF and other NF1-related tumors •Data sharing: • Eliminates hurdles for researchers • Enables stronger conclusions • Faciliates generation of new hypotheses • Allows the whole NF research community to participate, explore and ask new and different questions that may not have been tested •New discoveries may: • Answer our fundamental questions about cNF • Help researchers better identify and develop treatments

51. Sara Gosline, PhD Justin Guinney, PhD Xindi Guo Thomas Yu Annette Bakker, PhD Salvo La Rosa, PhD Pamela Knight, MS Hubert Weinberg, MD Nripesh Prasad, PhD Lu Le, MD-PhD robert.allaway@sagebase.org @allawayr

CTF 2017 Cutaneous Neurofibroma Resource Sage Bionetworks

CTF 2017 Cutaneous Neurofibroma Resource Sage Bionetworks

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