Recommendations for the Collection and Annotation of Biosamples for Analysis of Biomarkers in Neurofibromatosis and Schwannomatosis Clinical Trials

Overview

Journal Clin Trials

Publisher Sage Publications

Date 2023 Oct 31

PMID 37904489

Authors

R Taylor Sundby

Steven D Rhodes

Edina Komlodi-Pasztor

Herb Sarnoff

Vito Grasso

Meena Upadhyaya

AeRang Kim

D Gareth Evans

Jaishri O Blakeley

C Oliver Hanemann

Chetan Bettegowda

Affiliations

Soon will be listed here.

Abstract

Introduction: Neurofibromatosis 1 and schwannomatosis are characterized by potential lifelong morbidity and life-threatening complications. To date, however, diagnostic and predictive biomarkers are an unmet need in this patient population. The inclusion of biomarker discovery correlatives in neurofibromatosis 1/schwannomatosis clinical trials enables study of low-incidence disease. The implementation of a common data model would further enhance biomarker discovery by enabling effective concatenation of data from multiple studies.

Methods: The Response Evaluation in Neurofibromatosis and Schwannomatosis biomarker working group reviewed published data on emerging trends in neurofibromatosis 1 and schwannomatosis biomarker research and developed recommendations in a series of consensus meetings.

Results: Liquid biopsy has emerged as a promising assay for neurofibromatosis 1/schwannomatosis biomarker discovery and validation. In addition, we review recommendations for a range of biomarkers in clinical trials, neurofibromatosis 1/schwannomatosis-specific data annotations, and common data models for data integration.

Conclusion: These Response Evaluation in Neurofibromatosis and Schwannomatosis consensus guidelines are intended to provide best practices for the inclusion of biomarker studies in neurofibromatosis 1/schwannomatosis clinical trials, data, and sample annotation and to lay a framework for data harmonization and concatenation between trials.

Citing Articles

Contemporary Approach to Neurofibromatosis Type 1-Associated Malignant Peripheral Nerve Sheath Tumors.

Hirbe A, Dehner C, Dombi E, Eulo V, Gross A, Sundby T Am Soc Clin Oncol Educ Book. 2024; 44(3):e432242.

PMID: 38710002 PMC: 11656191. DOI: 10.1200/EDBK_432242.

Advancing neurofibromatosis and schwannomatosis clinical trial design: Consensus recommendations from the Response Evaluation in Neurofibromatosis and Schwannomatosis (REiNS) International Collaboration.

Merker V, Gross A, Widemann B, Plotkin S Clin Trials. 2023; 21(1):3-5.

PMID: 37776044 PMC: 10865758. DOI: 10.1177/17407745231201345.

References

Vrabel D, Sedlarikova L, Besse L, Rihova L, Bezdekova R, Almasi M . Dynamics of tumor-specific cfDNA in response to therapy in multiple myeloma patients. Eur J Haematol. 2019; 104(3):190-197. PMC: 7065130. DOI: 10.1111/ejh.13358. View

Henriksen T, Reinert T, Christensen E, Sethi H, Birkenkamp-Demtroder K, Gogenur M . The effect of surgical trauma on circulating free DNA levels in cancer patients-implications for studies of circulating tumor DNA. Mol Oncol. 2020; 14(8):1670-1679. PMC: 7400779. DOI: 10.1002/1878-0261.12729. View

Chen K, Zhao H, Shi Y, Yang F, Wang L, Kang G . Perioperative Dynamic Changes in Circulating Tumor DNA in Patients with Lung Cancer (DYNAMIC). Clin Cancer Res. 2019; 25(23):7058-7067. DOI: 10.1158/1078-0432.CCR-19-1213. View

Sims A, Smethurst G, Hey Y, Okoniewski M, Pepper S, Howell A . The removal of multiplicative, systematic bias allows integration of breast cancer gene expression datasets - improving meta-analysis and prediction of prognosis. BMC Med Genomics. 2008; 1:42. PMC: 2563019. DOI: 10.1186/1755-8794-1-42. View

Plotkin S, Messiaen L, Legius E, Pancza P, Avery R, Blakeley J . Updated diagnostic criteria and nomenclature for neurofibromatosis type 2 and schwannomatosis: An international consensus recommendation. Genet Med. 2022; 24(9):1967-1977. DOI: 10.1016/j.gim.2022.05.007. View

Eisenhauer E, Therasse P, Bogaerts J, Schwartz L, Sargent D, Ford R . New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer. 2008; 45(2):228-47. DOI: 10.1016/j.ejca.2008.10.026. View

Green A, Reeder-Hayes K, Corty R, Basch E, Milowsky M, Dusetzina S . The project data sphere initiative: accelerating cancer research by sharing data. Oncologist. 2015; 20(5):464-e20. PMC: 4425388. DOI: 10.1634/theoncologist.2014-0431. View

Sundby R, Pan A, Shern J . Liquid biopsies in pediatric oncology: opportunities and obstacles. Curr Opin Pediatr. 2021; 34(1):39-47. PMC: 8727502. DOI: 10.1097/MOP.0000000000001088. View

Bustin S, Benes V, Garson J, Hellemans J, Huggett J, Kubista M . The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin Chem. 2009; 55(4):611-22. DOI: 10.1373/clinchem.2008.112797. View

10.

Wilson G, Bryan J, Cranston K, Kitzes J, Nederbragt L, Teal T . Good enough practices in scientific computing. PLoS Comput Biol. 2017; 13(6):e1005510. PMC: 5480810. DOI: 10.1371/journal.pcbi.1005510. View

11.

Chaudhuri A, Chabon J, Lovejoy A, Newman A, Stehr H, Azad T . Early Detection of Molecular Residual Disease in Localized Lung Cancer by Circulating Tumor DNA Profiling. Cancer Discov. 2017; 7(12):1394-1403. PMC: 5895851. DOI: 10.1158/2159-8290.CD-17-0716. View

12.

Ceusters W, Smith B, Goldberg L . A terminological and ontological analysis of the NCI Thesaurus. Methods Inf Med. 2005; 44(4):498-507. View

13.

Rahman R, Ventz S, McDunn J, Louv B, Reyes-Rivera I, Polley M . Leveraging external data in the design and analysis of clinical trials in neuro-oncology. Lancet Oncol. 2021; 22(10):e456-e465. PMC: 8893120. DOI: 10.1016/S1470-2045(21)00488-5. View

14.

Abaza H, Kadioglu D, Martin S, Papadopoulou A, Dos Santos Vieira B, Schaefer F . Domain-Specific Common Data Elements for Rare Disease Registration: Conceptual Approach of a European Joint Initiative Toward Semantic Interoperability in Rare Disease Research. JMIR Med Inform. 2022; 10(5):e32158. PMC: 9166638. DOI: 10.2196/32158. View

15.

Johnson W, Li C, Rabinovic A . Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics. 2006; 8(1):118-27. DOI: 10.1093/biostatistics/kxj037. View

16.

Belenkaya R, Gurley M, Golozar A, Dymshyts D, Miller R, Williams A . Extending the OMOP Common Data Model and Standardized Vocabularies to Support Observational Cancer Research. JCO Clin Cancer Inform. 2021; 5:12-20. PMC: 8140810. DOI: 10.1200/CCI.20.00079. View

17.

Pauler D, Gower K, Goodman P, Crowley J, Thompson I . Biomarker-based methods for determining noncompliance in a prevention trial. Control Clin Trials. 2002; 23(6):675-85. DOI: 10.1016/s0197-2456(02)00231-3. View

18.

Wouters O, Mckee M, Luyten J . Estimated Research and Development Investment Needed to Bring a New Medicine to Market, 2009-2018. JAMA. 2020; 323(9):844-853. PMC: 7054832. DOI: 10.1001/jama.2020.1166. View

19.

Killock D . DYNAMIC insights into MRD responses early after resection of NSCLC. Nat Rev Clin Oncol. 2019; 16(11):661. DOI: 10.1038/s41571-019-0274-5. View

20.

Pellini B, Pejovic N, Feng W, Earland N, Harris P, Usmani A . ctDNA MRD Detection and Personalized Oncogenomic Analysis in Oligometastatic Colorectal Cancer From Plasma and Urine. JCO Precis Oncol. 2021; 5. PMC: 8232837. DOI: 10.1200/PO.20.00276. View